CN116580559A - Traffic data processing method based on artificial intelligence technology - Google Patents
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Abstract
The application relates to the technical field of artificial intelligence and discloses a traffic data processing method based on an artificial intelligence technology, wherein the traffic data processing method based on the artificial intelligence is applied to a vehicle-road cooperative system and comprises the steps of acquiring target traffic data of an intelligent terminal and sending the target traffic data to a road side base station; generating a target decision corresponding to the target traffic data through a preset decision generation model; and executing the target decision through the intelligent terminal to realize the processing of traffic data. Through the mode, the intelligent terminal receives the target traffic data sent by the intelligent terminal through the road side base station, generates the corresponding target decision through the preset decision generation model, and executes the target decision to realize the processing of the traffic data, so that the reliability of the traffic data processing in the automatic driving field is improved, and the technical problem of low reliability of the traffic data processing in the automatic driving field is solved.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a traffic data processing method based on an artificial intelligence technology.
Background
At present, with the continuous development of artificial intelligence technology and automobile technology, it is hoped that vehicles can replace people to complete more and more driving tasks through intelligent and networking, and safer, efficient, comfortable and energy-saving driving is realized. In the field of automatic driving, a vehicle-road cloud cooperative system is widely used, and the vehicle-road cloud cooperative system provides safety guarantee for automatic driving of vehicles. The method comprises the steps of sensing the surrounding environment of the vehicle through detection equipment such as a camera, a laser radar, a millimeter wave radar and the like which are arranged on the automatic driving vehicle, and controlling the automatic driving vehicle to run based on the sensed surrounding environment information. The state of the vehicle and some emergency conditions on the driving road cannot be uploaded to the vehicle through the vehicle-road cooperative system, so that the treatment of the emergency conditions is not timely enough, and the driving safety is affected. Therefore, how to improve the reliability of traffic data processing in the automatic driving field is a technical problem to be solved currently.
Disclosure of Invention
The application provides a traffic data processing method based on an artificial intelligence technology, which is used for improving the reliability of traffic data processing in the automatic driving field.
In a first aspect, the present application provides an artificial intelligence based traffic data processing method, including:
acquiring target traffic data of the intelligent terminal and sending the target traffic data to the road side base station;
generating a target decision corresponding to the target traffic data through a preset decision generation model;
and executing the target decision through the intelligent terminal to realize the processing of traffic data.
Further, before generating the target decision corresponding to the target traffic data through the preset decision generation model, the method further comprises:
acquiring historical traffic data and a historical decision, and respectively extracting a first data feature vector of the historical traffic data and a first decision feature vector of the historical decision;
respectively carrying out normalization processing on the first data feature vector and the first decision feature vector to obtain a normalized second data feature vector and a normalized second decision feature vector, and taking the second data feature vector and the normalized second decision feature vector as a training set;
and training the preset decision generation model through the LSTM and the training set.
Further, training the preset decision generation model through the long-term memory artificial neural network LSTM and the training set, further includes:
training the preset decision generation model based on a preset LSTM formula, the second data feature vector and the second decision feature vector, wherein the preset LSTM formula is:
for the ith said second data characteristic directionSimilarity of quantity to the ith said second decision feature vector,/th decision feature vector>For the similarity of the i-1 th said second data feature vector and the i-1 th said second decision feature vector +.>And the similarity mean value of all the second data feature vectors and all the second decision feature vectors.
Further, obtaining target traffic data of a target intelligent terminal and sending the target traffic data to the road side base station, including:
calculating the confidence coefficient of all traffic data of the target intelligent terminal through a preset confidence coefficient model;
traversing all the confidence degrees, and screening out traffic data corresponding to the confidence degrees which are not smaller than a preset confidence degree threshold value as the target traffic data.
Further, calculating the confidence coefficient of all traffic data of the target intelligent terminal through a preset confidence coefficient model comprises the following steps:
Calculating the Euclidean distance between each traffic data and the standard traffic data;
and calculating the confidence coefficient based on a preset formula and the Euclidean distance, wherein the preset formula is as follows:
p is the confidence and distance is the Euclidean distance.
Further, after generating the target decision corresponding to the target traffic data through the preset decision generation model, the method further comprises:
receiving traffic data of all intelligent terminals in a preset area through the road side base station, and generating traffic road condition data;
issuing the traffic road condition data to the target intelligent terminal to generate a traffic prompt decision;
and adjusting the target decision in response to the traffic prompt decision.
Further, the traffic road condition data is issued to the target intelligent terminal, and a traffic prompt decision is generated, which comprises the following steps:
calculating a target distance between the target vehicle and the traffic signal lamp;
generating the traffic prompt decision based on a preset vehicle speed optimization algorithm and the target distance, wherein a formula corresponding to the preset vehicle speed optimization algorithm is as follows:l is the target distance, a is the current acceleration of the target vehicle, V 0 And t is the reaction time of the target vehicle, and is the current speed of the target vehicle.
Further, the vehicle-road cooperative system further comprises a target vehicle, the intelligent terminal comprises a vehicle-mounted radar module and a perception module, and before generating a target decision corresponding to the target traffic data through a preset decision generation model, the intelligent terminal comprises:
detecting the running state of the target vehicle through the perception module to generate a target vehicle event;
and detecting the traffic scene of the target vehicle through the vehicle-mounted radar to generate a traffic scene event.
Further, generating, by a preset decision generation model, a target decision corresponding to the target traffic data, including:
when detecting that the target vehicle event and/or the traffic scene event is abnormal, sending a prompt signal;
and inputting the abnormal target vehicle event and/or the abnormal traffic scene event into the preset decision generation model to generate the target decision corresponding to the target vehicle event and/or the abnormal traffic scene event.
Further, executing, by the intelligent terminal, the target decision to implement processing of traffic data, including:
and determining a driving route and a driving mode corresponding to the intelligent terminal based on the target decision.
In a second aspect, the present application also provides an artificial intelligence based traffic data processing apparatus, the artificial intelligence based traffic data processing apparatus comprising:
the traffic data acquisition module is used for acquiring the target traffic data of the intelligent terminal and transmitting the target traffic data to the road side base station;
the decision generation module is used for generating a target decision corresponding to the target traffic data through a preset decision generation model;
and the decision execution module is used for executing the target decision through the intelligent terminal so as to realize the processing of traffic data.
In a third aspect, the present application also provides a computer device comprising a memory and a processor; the memory is used for storing a computer program; the processor is used for executing the computer program and realizing the traffic data processing method based on artificial intelligence when executing the computer program.
In a fourth aspect, the present application also provides a computer readable storage medium storing a computer program, which when executed by a processor causes the processor to implement an artificial intelligence based traffic data processing method as described above.
The application discloses a traffic data processing method based on an artificial intelligence technology, which is applied to a vehicle-road cooperative system and comprises the steps of acquiring target traffic data of an intelligent terminal and sending the target traffic data to a road side base station; generating a target decision corresponding to the target traffic data through a preset decision generation model; and executing the target decision through the intelligent terminal to realize the processing of traffic data. Through the mode, the intelligent terminal receives the target traffic data sent by the intelligent terminal through the road side base station, generates the corresponding target decision through the preset decision generation model, and executes the target decision to realize the processing of the traffic data, so that the reliability of the traffic data processing in the automatic driving field is improved, and the technical problem of low reliability of the traffic data processing in the automatic driving field is solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a traffic data processing method based on artificial intelligence according to a first embodiment of the present application;
FIG. 2 is a schematic flow chart of a traffic data processing method based on artificial intelligence according to a second embodiment of the present application;
FIG. 3 is a schematic flow chart of a traffic data processing method based on artificial intelligence according to a third embodiment of the present application;
FIG. 4 is a schematic flow chart of a traffic data processing method based on artificial intelligence according to a fourth embodiment of the present application;
FIG. 5 is a schematic block diagram of an artificial intelligence based traffic data processing device provided by an embodiment of the application;
fig. 6 is a schematic block diagram of a computer device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
It is to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification 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 the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
The embodiment of the application provides a traffic data processing method based on an artificial intelligence technology. The traffic data processing method based on the artificial intelligence can be applied to a server, receives target traffic data sent by an intelligent terminal through a road side base station, generates a corresponding target decision through a preset decision generation model, and executes the target decision by the intelligent terminal so as to realize the processing of the traffic data, improve the reliability of the traffic data processing in the automatic driving field and solve the technical problem of low reliability of the traffic data processing in the automatic driving field. The server may be an independent server or a server cluster.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic flow chart of a traffic data processing method based on artificial intelligence according to a first embodiment of the present application. The traffic data processing method based on the artificial intelligence can be applied to a server, is used for receiving the target traffic data sent by the intelligent terminal through the road side base station, generating a corresponding target decision through a preset decision generation model, and executing the target decision by the intelligent terminal so as to realize the processing of the traffic data, improve the reliability of the traffic data processing in the automatic driving field and solve the technical problem of low reliability of the traffic data processing in the automatic driving field.
As shown in fig. 1, the traffic data processing method based on artificial intelligence specifically includes steps S10 to S30.
S10, acquiring target traffic data of the intelligent terminal and sending the target traffic data to the road side base station;
in a specific embodiment, first, communication between the intelligent terminal and the roadside base station is established, and the mode may be a distributed network. The intelligent terminal can be a mobile phone, a car system and road side traffic facilities of a user, such as street lamps, monitoring and the like. When the intelligent terminal is a mobile phone or a car system of a user, traffic data can be a driving route of the user to a destination and buildings along the way, and whether the user is jammed on the route or not and whether facilities such as scenic spots, hotels and gas stations exist or not can be timely fed back to the user; and when the intelligent terminal is a road side traffic facility. The intelligent terminal can feed back whether the street lamp fails or not to the road side base station, and each intelligent terminal with traffic data such as illegal snapshot sends the collected traffic data to the road side base station.
Step S20, generating a target decision corresponding to the target traffic data through a preset decision generation model;
in a specific embodiment, corresponding target decisions are generated according to different types of traffic data. For example, when traffic data is that a front road is congested, a model is generated according to a corresponding decision, and another route with relatively good road conditions is determined; or when the current vehicle energy is detected to be insufficient, uploading traffic data to a road side base station, providing nearby gas stations or charging stations by the road side base station, and generating a corresponding decision route.
And step S30, executing the target decision through the intelligent terminal to realize the processing of traffic data.
In a specific embodiment, according to the type of the target decision, the corresponding intelligent terminal executes the target policy. For example, when the current running mode of the vehicle is an automatic driving mode, the received target decision is to change the running mode, and the user is reminded of actively driving.
The embodiment discloses a traffic data processing method based on an artificial intelligence technology, which is applied to a vehicle-road cooperative system and comprises the steps of acquiring target traffic data of an intelligent terminal and sending the target traffic data to a road side base station; generating a target decision corresponding to the target traffic data through a preset decision generation model; and executing the target decision through the intelligent terminal to realize the processing of traffic data. Through the mode, the intelligent terminal receives the target traffic data sent by the intelligent terminal through the road side base station, generates the corresponding target decision through the preset decision generation model, and executes the target decision to realize the processing of the traffic data, so that the reliability of the traffic data processing in the automatic driving field is improved, and the technical problem of low reliability of the traffic data processing in the automatic driving field is solved.
Referring to fig. 2, fig. 2 is a schematic flow chart of a traffic data processing method based on artificial intelligence according to a second embodiment of the present application. The traffic data processing method based on the artificial intelligence can be applied to a server, is used for receiving the target traffic data sent by the intelligent terminal through the road side base station, generating a corresponding target decision through a preset decision generation model, and executing the target decision by the intelligent terminal so as to realize the processing of the traffic data, improve the reliability of the traffic data processing in the automatic driving field and solve the technical problem of low reliability of the traffic data processing in the automatic driving field.
Based on the embodiment shown in fig. 1, in this embodiment, as shown in fig. 2, step S20 is preceded by steps S11 to S13.
Step S11, acquiring historical traffic data and a historical decision, and respectively extracting a first data feature vector of the historical traffic data and a first decision feature vector of the historical decision;
step S12, respectively carrying out normalization processing on the first data feature vector and the first decision feature vector to obtain a normalized second data feature vector and a normalized second decision feature vector, and taking the second data feature vector and the normalized second decision feature vector as a training set;
And step S13, training the preset decision generation model through the long-term and short-term memory artificial neural network LSTM and the training set.
In a specific embodiment, normalization is a dimensionless processing means to change the absolute value of the physical system value into a relative value relationship.
The training process of the decision generation model depends on an artificial intelligence technology, a digital twin platform or other cloud computing service platforms, and essentially, the purpose of training the current decision generation model is achieved by acquiring a large amount of historical data and determining a historical decision according to the feature vector classification of the historical data.
The embodiment discloses a traffic data processing method based on an artificial intelligence technology, which is applied to a vehicle-road cooperative system and comprises the steps of obtaining historical traffic data and taking the historical traffic data as a training set; extracting training feature vectors of the training set, and classifying based on the training feature vectors; and generating a corresponding training decision through the type of the preset decision generation model and the training feature vector, and finishing training of the preset decision generation model. By the method, a large amount of historical traffic data is acquired, corresponding training feature vectors are extracted, and different training decisions are generated after all training features are classified, so that the purpose of training a preset decision generation model is achieved, the reliability of traffic data processing in the automatic driving field is improved, and the technical problem of low reliability of traffic data processing in the automatic driving field is solved.
Further, step S13 further includes:
training the preset decision generation model based on a preset LSTM formula, the second data feature vector and the second decision feature vector, wherein the preset LSTM formula is:
for the similarity of the ith said second data feature vector to the ith said second decision feature vector,/i>For the similarity of the i-1 th said second data feature vector and the i-1 th said second decision feature vector +.>And the similarity mean value of all the second data feature vectors and all the second decision feature vectors.
In a specific embodiment, in a normal case, after a vehicle receives road sensing information detected in a coverage area sent by a road side device, the vehicle defaults that the received road sensing information is the road sensing information of the whole coverage area of the road side device, fuses the received road sensing information and the sensing information of the vehicle, and then controls the automatic driving vehicle to run according to the fused sensing data.
The vehicle-road cooperation is a junction of automatic driving and new construction, and the core of the vehicle-road cooperation is four parts of intelligent vehicle-mounted technology, intelligent road side technology, communication technology and cloud control technology. The cloud control technology requirements have the functions of data storage, calculation and decision making. The digital twin platform and cloud application mutually cooperate technology is applied, wherein the digital twin comprises a management platform and a data management platform. The management platform realizes the management of equipment access, monitoring and the like, the data management platform realizes the data storage data opening, authentication, life cycle management, unified data modeling and the like, and finally realizes the aims of controllable equipment and data management, controllable calculation and strategy execution and real-time synchronization and visualization of physical scene mapping.
The embodiment discloses a traffic data processing method based on an artificial intelligence technology, which is applied to a vehicle-road cooperative system and comprises the steps of detecting the running state of a target vehicle through a perception module to generate a target vehicle event; detecting a traffic scene of the target vehicle through the vehicle-mounted radar, generating a traffic scene event, and sending out a prompt signal when the target vehicle event is detected and/or the traffic scene event is abnormal; and inputting the abnormal target vehicle event and/or the abnormal traffic scene event into the preset decision generation model to generate the target decision corresponding to the target vehicle event and/or the abnormal traffic scene event. Through the mode, the intelligent terminal receives the target traffic data sent by the intelligent terminal through the road side base station, generates the corresponding target decision through the preset decision generation model, and executes the target decision to realize the processing of the traffic data, so that the reliability of the traffic data processing in the automatic driving field is improved, and the technical problem of low reliability of the traffic data processing in the automatic driving field is solved.
Referring to fig. 3, fig. 3 is a schematic flow chart of a traffic data processing method based on artificial intelligence according to a third embodiment of the present application. The traffic data processing method based on the artificial intelligence can be applied to a server, is used for receiving the target traffic data sent by the intelligent terminal through the road side base station, generating a corresponding target decision through a preset decision generation model, and executing the target decision by the intelligent terminal so as to realize the processing of the traffic data, improve the reliability of the traffic data processing in the automatic driving field and solve the technical problem of low reliability of the traffic data processing in the automatic driving field.
Based on the embodiment shown in fig. 1, in this embodiment, as shown in fig. 3, step S10 specifically includes steps S101 to S102.
Step S101, calculating the confidence coefficient of all traffic data of the target intelligent terminal through a preset confidence coefficient model;
step S102, traversing all the confidence degrees, and screening out traffic data corresponding to the confidence degrees which are not smaller than a preset confidence degree threshold value as the target traffic data.
Specifically, calculating the confidence coefficient of all traffic data of the target intelligent terminal through a preset confidence coefficient model comprises:
Calculating the Euclidean distance between each traffic data and the standard traffic data;
and calculating the confidence coefficient based on a preset formula and the Euclidean distance, wherein the preset formula is as follows:
p is the confidence and distance is the Euclidean distance.
Specifically, the similarity between the two data is characterized by the euclidean distance. For example, if the feature vector of the standard traffic data is (X, Y) and the feature vector uploaded by a certain intelligent terminal is (X1, Y1), the euclidean distance is calculated according to the calculation formula of the euclidean distance
In a specific embodiment, the confidence level is used for representing the influence degree of external environment information on automatic driving when the target strategy is used. For example, when the vehicle is traveling forward under the control of the target automatic driving strategy, an obstacle ahead of the vehicle traveling direction may be considered to have a greater degree of influence on the normal traveling of the vehicle, a pedestrian behind the vehicle traveling direction may be considered to have a smaller influence on the normal traveling of the vehicle, and so on. Or the size, position, movement track, movement parameters and the like of the obstacle can generate different degrees of influence on the target automatic driving strategy, the influence under different conditions is divided in advance, and the influence is stored in the system in a form of a mapping relation
The embodiment discloses a traffic data processing method based on an artificial intelligence technology, which comprises the steps of calculating the confidence coefficient of all traffic data of a target intelligent terminal through a preset confidence coefficient model; traversing all the confidence degrees, and screening out traffic data corresponding to the confidence degrees which are not smaller than a preset confidence degree threshold value as the target traffic data. Through the mode, the intelligent terminal receives the target traffic data sent by the intelligent terminal through the road side base station, generates the corresponding target decision through the preset decision generation model, and executes the target decision to realize the processing of the traffic data, so that the reliability of the traffic data processing in the automatic driving field is improved, and the technical problem of low reliability of the traffic data processing in the automatic driving field is solved.
Referring to fig. 4, fig. 4 is a schematic flow chart of a traffic data processing method based on artificial intelligence according to a fourth embodiment of the present application. The traffic data processing method based on the artificial intelligence can be applied to a server, is used for receiving the target traffic data sent by the intelligent terminal through the road side base station, generating a corresponding target decision through a preset decision generation model, and executing the target decision by the intelligent terminal so as to realize the processing of the traffic data, improve the reliability of the traffic data processing in the automatic driving field and solve the technical problem of low reliability of the traffic data processing in the automatic driving field.
Based on the embodiment shown in fig. 1, this embodiment specifically includes steps S21 to S23 after step S20 as shown in fig. 3.
S21, receiving traffic data of all intelligent terminals in a preset area through the road side base station, and generating traffic road condition data;
s22, the traffic road condition data is issued to the target intelligent terminal, and a traffic prompt decision is generated;
and step S23, responding to the traffic prompt decision, and adjusting the target decision.
Further, the step S22 includes:
calculating a target distance between the target vehicle and the traffic signal lamp;
generating the traffic prompt decision based on a preset vehicle speed optimization algorithm and the target distance, wherein a formula corresponding to the preset vehicle speed optimization algorithm is as follows:l is the target distance, a is the current acceleration of the target vehicle, V 0 And t is the reaction time of the target vehicle, and is the current speed of the target vehicle.
Based on the embodiment shown in fig. 1, step S20 includes, before:
detecting the running state of the target vehicle through the perception module to generate a target vehicle event;
and detecting the traffic scene of the target vehicle through the vehicle-mounted radar to generate a traffic scene event.
Based on the above embodiment, in this embodiment, the step S20 includes:
when detecting that the target vehicle event and/or the traffic scene event is abnormal, sending a prompt signal;
and inputting the abnormal target vehicle event and/or the abnormal traffic scene event into the preset decision generation model to generate the target decision corresponding to the target vehicle event and/or the abnormal traffic scene event.
Based on all the above embodiments, in this embodiment, the step S30 includes:
and determining a driving route and a driving mode corresponding to the intelligent terminal based on the target decision.
Specifically, for example, when the target vehicle normally runs on the target route, the road side base station receives the preset area, and some emergency accidents or traffic jams, such as landslide, vehicle accidents and the like, occur on the target route, or the road side base station calculates the current moment or a moment in the future according to historical data, and when the target route is jammed and extreme weather occurs, prompt signals are timely sent to each vehicle on the route to remind each vehicle to detour in time, so that the jam is avoided and even the accidents are avoided.
Referring to fig. 5, fig. 5 is a schematic block diagram of an artificial intelligence-based traffic data processing apparatus for performing the aforementioned artificial intelligence-based traffic data processing method according to an embodiment of the present application. Wherein, the traffic data processing device based on artificial intelligence can be configured on a server.
As shown in fig. 5, the traffic data processing apparatus 400 based on artificial intelligence includes:
the traffic data acquisition module 410 is configured to acquire target traffic data of the intelligent terminal, and send the target traffic data to the roadside base station;
the decision generation module 420 is configured to generate a target decision corresponding to the target traffic data through a preset decision generation model;
and the decision execution module 430 is configured to execute the target decision through the intelligent terminal to implement processing of traffic data.
Further, the traffic data processing device based on artificial intelligence further comprises:
the feature vector extraction module is used for acquiring historical traffic data and historical decisions and respectively extracting a first data feature vector of the historical traffic data and a first decision feature vector of the historical decisions;
the training set acquisition module is used for respectively carrying out normalization processing on the first data feature vector and the first decision feature vector to obtain a normalized second data feature vector and a normalized second decision feature vector, and taking the second data feature vector and the second decision feature vector as training sets;
and the decision generation model training module is used for training the preset decision generation model through the long-short-term memory artificial neural network LSTM and the training set.
Further, the decision generation model training comprises:
the decision generation model training unit is configured to train the preset decision generation model based on a preset LSTM formula, the second data feature vector, and the second decision feature vector, where the preset LSTM formula is:
for the similarity of the ith said second data feature vector to the ith said second decision feature vector,/i>For the similarity of the i-1 th said second data feature vector and the i-1 th said second decision feature vector +.>And the similarity mean value of all the second data feature vectors and all the second decision feature vectors.
Further, the traffic data acquisition module 410 includes:
the confidence coefficient calculating unit is used for calculating the confidence coefficient of all traffic data of the target intelligent terminal through a preset confidence coefficient model;
and the target traffic data determining unit is used for traversing all the confidence degrees and screening out traffic data corresponding to the confidence degrees which are not smaller than a preset confidence degree threshold value as the target traffic data.
Further, the confidence calculating unit includes:
the Euclidean distance calculating subunit is used for calculating Euclidean distance between each traffic data and standard traffic data;
The confidence coefficient calculating subunit is configured to calculate the confidence coefficient based on a preset formula and the euclidean distance, where the preset formula is:
p is the confidence and distance is the Euclidean distance. .
Further, the traffic data processing device based on artificial intelligence further comprises:
the traffic road condition data generation module is used for receiving traffic data of all the intelligent terminals in a preset area through the road side base station and generating traffic road condition data;
the traffic prompt decision generation module is used for transmitting the traffic road condition data to the target intelligent terminal to generate a traffic prompt decision;
and the target decision adjustment module is used for responding to the traffic prompt decision and adjusting the target decision.
Further, the traffic prompt decision generation module includes:
a target distance calculating unit for calculating a target distance between the target vehicle and the traffic signal lamp;
the vehicle speed optimizing unit is used for generating the traffic prompt decision based on a preset vehicle speed optimizing algorithm and the target distance, wherein the preset vehicle speed optimizing algorithm corresponds to the formula:l is the target distance, a is the current acceleration of the target vehicle, V 0 And t is the reaction time of the target vehicle, and is the current speed of the target vehicle.
Further, the traffic data processing device based on artificial intelligence further comprises:
the vehicle event generation module is used for detecting the running state of the target vehicle through the sensing module to generate a target vehicle event;
and the traffic scene event generation module is used for detecting the traffic scene of the target vehicle through the vehicle-mounted radar and generating a traffic scene event.
Further, the decision generation module 420 includes:
the abnormal prompt signal unit is used for sending a prompt signal when detecting that the target vehicle event and/or the traffic scene event is abnormal;
the target decision generation unit is used for inputting the abnormal target vehicle event and/or the abnormal traffic scene event into the preset decision generation model and generating the target decision corresponding to the target vehicle event and/or the abnormal traffic scene event.
Further, the decision execution module 430 includes:
and the decision execution unit is used for determining a driving route and a driving mode corresponding to the intelligent terminal based on the target decision.
It should be noted that, for convenience and brevity of description, the specific working process of the apparatus and each module described above may refer to the corresponding process in the foregoing method embodiment, which is 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.
With reference to FIG. 6, the computer device includes a processor, memory, and a network interface connected by a system bus, where the memory may include a non-volatile storage medium and an internal memory.
The non-volatile storage medium may store an operating system and a computer program. The computer program comprises program instructions that, when executed, cause the processor to perform any of a variety of artificial intelligence based traffic data processing methods.
The processor is used to provide computing and control capabilities to support the operation of the entire computer device.
The internal memory provides an environment for the execution of a computer program in a non-volatile storage medium that, when executed by a processor, causes the processor to perform any of a variety of artificial intelligence based traffic data processing methods.
The network interface is used for network communication such as transmitting assigned tasks and the like. It will be appreciated by those skilled in the art that the structure shown in FIG. 6 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
It should be appreciated that the processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the 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 run a computer program stored in the memory to implement the steps of:
Acquiring target traffic data of the intelligent terminal and sending the target traffic data to the road side base station;
generating a target decision corresponding to the target traffic data through a preset decision generation model;
and executing the target decision through the intelligent terminal to realize the processing of traffic data.
In one embodiment, before generating the target decision corresponding to the target traffic data by the preset decision generation model, the method is further used for realizing:
acquiring historical traffic data and a historical decision, and respectively extracting a first data feature vector of the historical traffic data and a first decision feature vector of the historical decision;
respectively carrying out normalization processing on the first data feature vector and the first decision feature vector to obtain a normalized second data feature vector and a normalized second decision feature vector, and taking the second data feature vector and the normalized second decision feature vector as a training set;
and training the preset decision generation model through the LSTM and the training set.
In one embodiment, training the preset decision generation model by the long-short term memory artificial neural network LSTM and the training set is further used to implement:
Training the preset decision generation model based on a preset LSTM formula, the second data feature vector and the second decision feature vector, wherein the preset LSTM formula is:
for the similarity of the ith said second data feature vector to the ith said second decision feature vector,/i>For the similarity of the i-1 th said second data feature vector and the i-1 th said second decision feature vector +.>And the similarity mean value of all the second data feature vectors and all the second decision feature vectors.
In one embodiment, target traffic data of a target intelligent terminal is acquired and sent to the road side base station, so as to realize:
calculating the confidence coefficient of all traffic data of the target intelligent terminal through a preset confidence coefficient model;
traversing all the confidence degrees, and screening out traffic data corresponding to the confidence degrees which are not smaller than a preset confidence degree threshold value as the target traffic data.
In one embodiment, the confidence coefficient of all traffic data of the target intelligent terminal is calculated through a preset confidence coefficient model, and is used for realizing:
calculating the Euclidean distance between each traffic data and the standard traffic data;
and calculating the confidence coefficient based on a preset formula and the Euclidean distance, wherein the preset formula is as follows:
P is the confidence and distance is the Euclidean distance.
In one embodiment, after generating the target decision corresponding to the target traffic data by the preset decision generation model, the method is further used for realizing:
receiving traffic data of all intelligent terminals in a preset area through the road side base station, and generating traffic road condition data;
issuing the traffic road condition data to the target intelligent terminal to generate a traffic prompt decision;
and adjusting the target decision in response to the traffic prompt decision.
In one embodiment, the traffic road condition data is issued to the target intelligent terminal, and a traffic prompt decision is generated for realizing:
calculating a target distance between the target vehicle and the traffic signal lamp;
generating the traffic prompt decision based on a preset vehicle speed optimization algorithm and the target distance, wherein a formula corresponding to the preset vehicle speed optimization algorithm is as follows:l is the target distance, a is the current acceleration of the target vehicle, V 0 And t is the reaction time of the target vehicle, and is the current speed of the target vehicle.
In one embodiment, before generating the target decision corresponding to the target traffic data by a preset decision generation model, the method is used for realizing:
Detecting the running state of the target vehicle through the perception module to generate a target vehicle event;
and detecting the traffic scene of the target vehicle through the vehicle-mounted radar to generate a traffic scene event.
In one embodiment, a target decision corresponding to the target traffic data is generated by a preset decision generation model for implementation:
when detecting that the target vehicle event and/or the traffic scene event is abnormal, sending a prompt signal;
and inputting the abnormal target vehicle event and/or the abnormal traffic scene event into the preset decision generation model to generate the target decision corresponding to the target vehicle event and/or the abnormal traffic scene event.
In one embodiment, the target decision is performed by the intelligent terminal to implement processing of traffic data, for implementing:
determining a driving route and a driving mode corresponding to the intelligent terminal based on the target decision
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, the computer program comprises program instructions, and the processor executes the program instructions to realize any traffic data processing method based on artificial intelligence.
The computer readable storage medium may be an internal storage unit of the computer device according to 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), or the like, which are provided on the computer device.
While the application has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (10)
1. The traffic data processing method based on the artificial intelligence is characterized by being applied to a vehicle-road cooperation system, wherein the vehicle-road cooperation system comprises a road side base station and an intelligent terminal, and the traffic data processing method based on the artificial intelligence comprises the following steps:
Acquiring target traffic data of the intelligent terminal and sending the target traffic data to the road side base station;
generating a target decision corresponding to the target traffic data through a preset decision generation model;
and executing the target decision through the intelligent terminal to realize the processing of traffic data.
2. The artificial intelligence based traffic data processing method according to claim 1, wherein before the generating the target decision corresponding to the target traffic data by the preset decision generation model, further comprising:
acquiring historical traffic data and a historical decision, and respectively extracting a first data feature vector of the historical traffic data and a first decision feature vector of the historical decision;
respectively carrying out normalization processing on the first data feature vector and the first decision feature vector to obtain a normalized second data feature vector and a normalized second decision feature vector, and taking the second data feature vector and the normalized second decision feature vector as a training set;
and training the preset decision generation model through the LSTM and the training set.
3. The traffic data processing method according to claim 2, wherein training the preset decision generation model by the long-short-term memory artificial neural network LSTM and the training set further comprises:
Training the preset decision generation model based on a preset LSTM formula, the second data feature vector and the second decision feature vector, wherein the preset LSTM formula is:
for the similarity of the ith said second data feature vector to the ith said second decision feature vector,/i>For the similarity of the i-1 th said second data feature vector and the i-1 th said second decision feature vector +.>And the similarity mean value of all the second data feature vectors and all the second decision feature vectors.
4. The traffic data processing method according to claim 1, wherein the obtaining the target traffic data of the target intelligent terminal and transmitting the target traffic data to the roadside base station comprises:
calculating the confidence coefficient of all traffic data of the target intelligent terminal through a preset confidence coefficient model;
traversing all the confidence degrees, and screening out traffic data corresponding to the confidence degrees which are not smaller than a preset confidence degree threshold value as the target traffic data.
5. The traffic data processing method according to claim 4, wherein the calculating the confidence of all traffic data of the target intelligent terminal by the preset confidence model includes:
Calculating the Euclidean distance between each traffic data and the standard traffic data;
and calculating the confidence coefficient based on a preset formula and the Euclidean distance, wherein the preset formula is as follows:
p is the confidence and distance is the Euclidean distance.
6. The traffic data processing method according to claim 1, wherein after the generating the target decision corresponding to the target traffic data by the preset decision generation model, further comprising:
receiving traffic data of all intelligent terminals in a preset area through the road side base station, and generating traffic road condition data;
issuing the traffic road condition data to the target intelligent terminal to generate a traffic prompt decision;
and adjusting the target decision in response to the traffic prompt decision.
7. The method for processing traffic data based on artificial intelligence according to claim 6, wherein the intelligent terminal comprises a target vehicle and a traffic signal lamp, the issuing the traffic condition data to the target intelligent terminal generates a traffic prompt decision, and the method comprises:
calculating a target distance between the target vehicle and the traffic signal lamp;
Generating the traffic prompt decision based on a preset vehicle speed optimization algorithm and the target distance, wherein a formula corresponding to the preset vehicle speed optimization algorithm is as follows:l is the target distance, a is the target vehicleCurrent acceleration of V 0 And t is the reaction time of the target vehicle, and is the current speed of the target vehicle.
8. The traffic data processing method based on artificial intelligence according to claim 1, wherein the vehicle-road coordination system further comprises a target vehicle, the intelligent terminal comprises a vehicle-mounted radar module and a perception module, and before the target decision corresponding to the target traffic data is generated through a preset decision generation model, the method comprises:
detecting the running state of the target vehicle through the perception module to generate a target vehicle event;
and detecting the traffic scene of the target vehicle through the vehicle-mounted radar to generate a traffic scene event.
9. The traffic data processing method according to claim 8, wherein the intelligent terminal includes a vehicle-mounted radar module and a perception module, the generating a target decision corresponding to the target traffic data through a preset decision generation model includes:
When detecting that the target vehicle event and/or the traffic scene event is abnormal, sending a prompt signal;
and inputting the abnormal target vehicle event and/or the abnormal traffic scene event into the preset decision generation model to generate the target decision corresponding to the target vehicle event and/or the abnormal traffic scene event.
10. The traffic data processing method according to claim 1, wherein the executing, by the intelligent terminal, the target decision to implement the processing of traffic data comprises:
and determining a driving route and a driving mode corresponding to the intelligent terminal based on the target decision.
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