KR102136400B1 - System and method for agent of smart driving - Google Patents

Abstract

The smart driving agent system based on the vehicle's driving information big data and virtual physics system according to the present invention includes a communication module that transmits and receives data to and from the vehicle, safety information and fuel efficiency information, and provides an optimal driving model for controlling the vehicle. It includes a memory for storing a program for generating and a processor for executing the program stored in the memory. At this time, as the processor executes the program, a virtual physical system is generated based on driving information big data collected from the vehicle or a vehicle of the same model as the vehicle and characteristic data of the vehicle, and the virtual physical system Generates the optimal driving model through learning based on an artificial intelligence learning algorithm with respect to the simulation result, and provides the generated optimal driving model to the vehicle through the communication module, wherein the driving information big data is the It includes at least one of driving and driving information, driving road traffic information, and driving road attribute information.

Description

Smart driving agent system and method{SYSTEM AND METHOD FOR AGENT OF SMART DRIVING}

The present invention relates to a smart driving agent system and method based on a vehicle's driving information big data and a virtual physical system.

Recently, a large number of human accidents caused by large buses have occurred and have emerged as a serious social problem. There is a problem that the accident caused by such a large bus leads to a large accident once an accident occurs because several passengers are boarding.

In addition, the number of elderly drivers over the age of 65 who drive business vehicles is steadily increasing every year. In the case of such an elderly driver, since the ability to cope with the accident is forced to decrease gradually, the possibility of causing an accident increases as much, so it is necessary to develop a safe driving support device that prevents an accident caused by an elderly driver and provides a safe driving environment. State.

In addition, in the case of a business vehicle, there is a problem that driving for reducing fuel efficiency is not performed well. Fuel efficiency driving through optimal driving has the effect of reducing greenhouse gases by about 10%. Fuel economy driving reduces risk of accidents such as rapid acceleration and deceleration by 40-50%, so it reduces the risk of accidents by one tenth. It has the effect of lowering it to a level. Accordingly, it is necessary to induce fuel efficiency driving for reducing greenhouse gas emissions and preventing accidents in business vehicles.

In this regard, Korean Patent Publication No. 10-2014-0094288 (invention name: cruise control speed control device and method using vehicle driving mode) converts to eco mode, normal mode and sports mode through input for each vehicle mode. Technology.

An embodiment of the present invention is to provide a smart driving agent system and method that can reduce fuel efficiency and improve stability by controlling optimal driving of a vehicle by using the vehicle's driving information big data and a virtual physical system.

However, the technical problem to be achieved by the present embodiment is not limited to the technical problem as described above, and other technical problems may exist.

As a technical means for achieving the above technical problem, the smart driving agent system based on the driving information big data and the virtual physics system of the vehicle according to the first aspect of the present invention is a communication module for transmitting and receiving data with the vehicle, safety information And a memory that stores fuel consumption information and a program for generating an optimal driving model for controlling the vehicle, and a processor that executes the program stored in the memory. At this time, as the processor executes the program, a virtual physical system is generated based on driving information big data collected from the vehicle or a vehicle of the same model as the vehicle and characteristic data of the vehicle, and the virtual physical system Generates the optimal driving model through learning based on an artificial intelligence learning algorithm with respect to the simulation result, and provides the generated optimal driving model to the vehicle through the communication module, wherein the driving information big data is the It includes one or more of driving and driving information, driving road traffic information, and driving road attribute information.

The processor generates a virtual model of a driving road based on the property information of the driving road, generates a virtual model of the vehicle based on the characteristic data of the vehicle, and generates a virtual model of the vehicle on the road configured through the driving road virtual model. The vehicle can be configured to generate the virtual physics system.

The processor is a primary driving model that includes the fuel efficiency information for a road constructed through the virtual model of the driving road through learning based on the artificial intelligence learning algorithm to simulate a result of simulating the virtual physical system for driving of the vehicle. You can create

The processor performs a result of performing the simulation on driving of a surrounding vehicle based on an average vehicle speed for each road section extracted from the driving information big data for the primary driving model through learning based on the artificial intelligence learning algorithm, It can be generated as a secondary driving model.

The processor generates a virtual model of the driving environment based on the driving and driving information of the vehicle and traffic information of the driving road, and applies the generated driving virtual model to the virtual physics system to simulate the results of the artificial intelligence learning algorithm. Based on the learning, the secondary driving model may be generated as the optimal driving model.

The characteristic data may include power train information of the vehicle and structural information of the vehicle.

The driving and driving information of the vehicle includes at least one of driving date and time, driving section, driving position, speed, and real-time fuel efficiency data information, and the driving road traffic information includes at least one of traffic volume and average speed information, and the driving The road attribute information may include one or more of slope, curvature, speed limit, road type, entry and exit section, and branch section information.

The processor obtains the driving speed information of the vehicle and matches the position information of the vehicle with a 3D road map to determine road slope, curvature, speed limit, type, entry and exit section, and road section information including branch section information. After acquiring the driving position information of, the fuel efficiency information may be learned based on the driving speed information corresponding to the driving position information of the vehicle.

The processor may acquire driving speed information of the vehicle including current driving speed information of the vehicle, speed limit information of the driving road of the vehicle, and average speed information of surrounding vehicles.

The processor may generate the optimum driving model that satisfies a case in which the difference between the target fuel efficiency and the fuel efficiency for each driving condition is minimal, and the difference between the target safety and the safety degree for each driving condition is minimal based on the driving information big data. have.

In addition, the smart driving agent method based on the driving information big data and the virtual physics system of the vehicle according to the second aspect of the present invention includes driving information big data and characteristic data of the vehicle collected from the vehicle or a vehicle of the same model as the vehicle Generating a virtual physics system based on the; Simulating the virtual physics system; And generating an optimal driving model through learning based on an artificial intelligence learning algorithm with respect to the simulation result, and providing the optimal driving model to the vehicle. At this time, the driving information big data includes one or more of driving and driving information of the vehicle, driving road traffic information, and driving road attribute information, and the characteristic data includes power train information of the vehicle and structural information of the vehicle do.

According to any one of the above-described problem solving means of the present invention, by generating and applying a virtual physics system taking into account not only driving information big data but also vehicle characteristic data together, it was dependent on the amount and quality of learning data when generating the optimal driving model. You can solve the problem.

In addition, it is possible to overcome the limitations of the prior art that is limited to vehicle interior information and controls the vehicle.

In addition, through the smart driving agent, it is possible to increase the convenience of the driver and to prevent a traffic accident by promoting safe driving, and at the same time, it is possible to expect an effect of reducing fuel costs and emissions.

1 is a view schematically illustrating a smart driving agent system according to an embodiment of the present invention.
2 is a block diagram of a smart driving agent system according to an embodiment of the present invention.
3 is a flowchart of a smart driving agent method according to an embodiment of the present invention.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art to which the present invention pertains may easily practice. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. In addition, in order to clearly describe the present invention, parts not related to the description are omitted.

When a certain part of the specification "includes" a certain component, it means that other components may be further included rather than excluding other components, unless otherwise specified.

One embodiment of the present invention relates to a smart driving agent system 100 and a method based on a vehicle's driving information big data and a virtual physics system.

In the case of the prior art, in order to improve driving performance and fuel efficiency, engine characteristics of a vehicle such as engine torque and engine efficiency map, transmission gear ratio, vehicle weight, etc. are considered, or a normal mode or an eco mode according to a driver's preference. It allows you to select sports modes, etc.

However, in the case of the prior art, there is a problem that the vehicle driving control algorithm is applied in consideration of only the inside information of the vehicle, and there is a problem that there is no consideration of environment information outside the vehicle such as characteristics of a driving road or real-time traffic.

Particularly, in the cruise mode, since the driver strictly follows the speed selected by the driver, anxiety in a curved section is generated, and there is a problem that a fuel efficiency saving effect is lower than that directly controlled by the driver.

On the other hand, in one embodiment of the present invention, vehicle driving and driving information, driving road traffic information and driving road property information in consideration of vehicle driving data big data, vehicle power train information, and vehicle characteristic data including vehicle structural information By learning the algorithm based on, it is possible to create an optimal driving model that can improve safety and fuel economy.

1 is a diagram schematically illustrating a smart driving agent system 100 according to an embodiment of the present invention. 2 is a block diagram of a smart driving agent system 100 according to an embodiment of the present invention.

The smart driving agent system 100 according to an embodiment of the present invention may be connected to one or more vehicles 200 and surrounding facilities 300 through a network. Here, the network means a connection structure capable of exchanging information between each node, such as terminals and servers, and an example of such a network is a 3rd Generation Partnership Project (3GPP) network, and Long Term Evolution (LTE). ) Network, World Interoperability for Microwave Access (WIMAX) network, Internet, Local Area Network (LAN), Wireless Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), Bluetooth ( Bluetooth) network, satellite broadcasting network, analog broadcasting network, DMB (Digital Multimedia Broadcasting) network, WiFi, V2X, V2V, and the like.

At this time, the vehicle of one embodiment of the present invention may be a commercial vehicle that repeatedly runs a predetermined time and route for a predetermined period or more, such as a high-speed bus or a large cargo vehicle. That is, an embodiment of the present invention can generate and provide an optimal driving model capable of optimizing the driving pattern of the vehicle by analyzing the characteristic data of the commercial vehicle and the big data accumulated for a long period of time in the commercial vehicle. have.

The smart driving agent system 100 according to an embodiment of the present invention includes a communication module 110, a memory 120, and a processor 130. At this time, the smart driving agent system 100 according to an embodiment of the present invention is configured as a high-performance server for analyzing driving data big data collected from a vehicle or a vehicle of the same model and surrounding vehicles of the same model through an artificial intelligence learning algorithm In this case, it may be configured independently of the vehicle, but is not necessarily limited thereto, and may be configured integrally with the vehicle in some cases.

The communication module 110 receives the vehicle driving information big data and characteristic data from the vehicle and provides an optimal driving model as the vehicle. At this time, the information received through the communication module 110 is the vehicle interior information (speed, location, wheel speed, fuel efficiency information, etc.) sensed from the sensor unit in the vehicle, the surrounding vehicle or traffic information system located in the vicinity of the host vehicle, weather Data from an information system or the like.

In the memory 120, a program for generating an optimal driving model including safety information and fuel efficiency information is stored. Here, the memory 120 is a non-volatile storage device and a volatile storage device that keeps stored information even when power is not supplied.

For example, the memory 120 is a compact flash (CF) card, a secure digital (SD) card, a memory stick, a solid-state drive (SSD), and a micro SD NAND flash memory such as cards, magnetic computer storage devices such as hard disk drives (HDDs), and optical disc drives such as CD-ROMs, DVD-ROMs, and the like. Can.

As the processor 130 executes a program stored in the memory 120, a virtual physics system (CPS, Cyber-Physical) is based on driving information big data and vehicle characteristic data collected from a vehicle or a vehicle of the same model as the vehicle. System), and the optimal driving model including safety and fuel efficiency information through learning based on the artificial intelligence learning algorithm for the simulation result of the virtual physics system, and the optimal driving model through the communication module 110. Provided by vehicle.

The vehicle that has received the optimal driving model can control the driving of the vehicle according to the optimum safety level and fuel efficiency information based on this.

For reference, the components shown in FIG. 1 according to an embodiment of the present invention may be implemented in software or in a hardware form such as a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC), and perform predetermined roles. can do.

However,'components' are not limited to software or hardware, and each component may be configured to be in an addressable storage medium or may be configured to reproduce one or more processors.

Thus, as an example, a component is a component, such as software components, object-oriented software components, class components, and task components, processes, functions, attributes, procedures, subs It includes routines, segments of program code, drivers, firmware, microcode, circuitry, data, database, data structures, tables, arrays and variables.

Components and functions provided within those components may be combined into a smaller number of components or further separated into additional components.

Hereinafter, a smart driving agent method in the smart driving agent system 100 according to an embodiment of the present invention will be described in detail with reference to FIG. 3.

3 is a flowchart of a smart driving agent method according to an embodiment of the present invention.

The smart driving agent method according to an embodiment of the present invention generates a virtual physics system on the basis of driving information big data and characteristic data of a vehicle received and stored from a vehicle or a vehicle of the same model through a communication module (S110). .

In this case, the stored and analyzed driving information big data may include one or more of vehicle driving and driving information, driving road traffic information, and driving road attribute information.

For example, vehicle operation and driving information may include one or more of a driving date and time, a driving section, a driving position, a speed, and real-time fuel efficiency data information.

The road traffic information may include one or more of traffic volume and average speed information.

In addition, the driving road attribute information may include one or more of slope, curvature, speed limit, road type, entry and exit section, and branch section information.

On the other hand, when only the driving information big data is learned through the artificial intelligence learning algorithm to generate the optimal driving model, the performance of the smart driving agent system is entirely dependent on the performance of the optimal driving model generated through learning.

Therefore, the amount and quality of the driving information big data of a commercial vehicle plays a very important role in determining the performance of the optimal driving model. At this time, if the amount of driving information big data is insufficient or there is a lot of noise, it is generated through the learning process. The performance of the optimal driving model can be as low as expected.

In order to solve this problem, the smart driving agent system according to an embodiment of the present invention is characterized by generating a virtual physical system by reflecting not only driving information big data but also vehicle characteristic data together. That is, an embodiment of the present invention configures a simulation environment in which a virtual traffic information condition is input to a virtual driving road, and then generates an optimal driving model by performing learning based on an artificial intelligence learning algorithm on the simulation result. Can.

At this time, the characteristic data of the vehicle is data that can best represent the driving characteristics of the commercial vehicle, and the characteristic data may include power train information of the vehicle and structural information of the vehicle.

Here, the vehicle's power train information may include engine torque, efficiency map, transmission map, and gear ratio information, and the vehicle's structural information includes information such as the vehicle's suspension/braking/steering system design values, hard points, and weight. can do.

On the other hand, the virtual physics system generated by the processor 130 is for learning to simulate vehicle driving and find the optimal driving model, and includes a vehicle virtual model based on real physical characteristics, and a virtual model of a driving road and a virtual model of the driving environment. can do.

The processor 130 may first generate a virtual model of the driving road based on the driving road attribute information included in the driving information big data, and generate a vehicle virtual model based on the vehicle characteristic data. Then, the processor 130 may generate a virtual physical system by constructing a vehicle according to the vehicle virtual model on the road configured through the virtual model of the driving road.

Such a vehicle virtual model may include an engine and transmission model, a vehicle dynamics model, and the driving road virtual model may include a road 3D model and road speed limit information, and the driving environment virtual model includes traffic by time and day, average by section Speed information may be included.

Next, the processor 130 simulates the virtual physics system generated as described above (S120), and generates an optimal driving model through learning based on an artificial intelligence learning algorithm for the simulation result (S130).

Specifically, the processor 130 performs a first driving model including fuel efficiency information on a road constructed through a virtual model of a driving road through learning based on an artificial intelligence learning algorithm to simulate a result of simulating a virtual physical system for driving of a vehicle. Can be created.

In addition, the processor 130 performs a simulation on driving of the driving vehicle based on the average vehicle speed for each road section extracted from the driving information big data for the primary driving model through learning based on an artificial intelligence learning algorithm, 2 It can be created as a car driving model.

In addition, the processor 130 may generate a virtual model of the driving environment based on the driving and driving information of the vehicle and traffic information of the driving road and apply the virtual model to the virtual physical system. Accordingly, the processor 130 may generate a secondary driving model as an optimal driving model through learning based on an artificial intelligence learning algorithm by simulating a virtual physical system to which a driving environment virtual model is applied.

On the other hand, the smart driving agent method according to an embodiment of the present invention can apply any one of machine learning, deep learning and deep reinforcement learning as an artificial intelligence learning algorithm to derive an optimal driving model.

At this time, an embodiment of the present invention can derive the optimal driving pattern for each road section, monthly/day/week/time according to the vehicle through the artificial intelligence learning algorithm described above, safety information and fuel efficiency based on the optimal driving pattern An optimal driving model including information can be generated.

Here, the safety level information may be quantitatively comparable to, for example, the dangerous driving behavior standards for each vehicle provided by the Korea Transportation Safety Authority as shown in Table 1 below.

In addition, the fuel efficiency information refers to a quantitative value of the amount of fuel consumed when driving in the same section.

[Table 1]

First, the description of generating the optimal driving model by analyzing driving information big data using machine learning is as follows.

Specifically, when using machine learning as an artificial intelligence learning algorithm in one embodiment of the present invention, the following features should be considered.

For example, in a commercial vehicle in which the weight of the vehicle itself or the weight of the load is larger than that of a general passenger car, the fuel efficiency change according to driving speed and deceleration/acceleration is very large. In particular, apart from the longitudinal motion characteristic, the transverse motion characteristic has a characteristic that its influence is insignificant.

In addition, there is a characteristic that the driving speed of a commercial vehicle traveling on an automobile exclusive road is greatly affected by the speed limit of the driving road and the average speed of the surrounding vehicles.

In addition, acceleration and deceleration have characteristics that are mainly influenced by entering and exiting other vehicles in the front of the driving lane, which occurs particularly frequently in the entry and exit sections of the expressway and the branch section.

In addition, there is no other factor, there is a characteristic that the slope and curvature of the driving road affects fuel efficiency even in a commercial vehicle that is driving at a constant speed.

According to this characteristic, in the case of applying machine learning with an artificial intelligence learning algorithm, an embodiment of the present invention first acquires characteristics of vehicle driving speed information in order to generate an optimal driving model.

Then, the location information of the vehicle according to the GPS information is matched with the 3D road map to obtain the characteristics of the driving position information in the road information including the slope, curvature, speed limit, type, entry and exit section, and branch section information of the road. do.

After obtaining such characteristics, an optimum driving model according to a driving section for improving fuel efficiency may be generated by learning fuel efficiency information based on driving speed information corresponding to driving position information of the vehicle.

As the optimal driving model is generated, one embodiment of the present invention may quantitatively evaluate safety through comparison with the dangerous driving behavior criteria according to Table 1 above.

Next, the contents of generating the optimal driving model by learning the results of the simulation of the virtual physics system using deep learning are described as follows.

In the case of in-depth learning, unlike machine learning, there is a feature that it is not necessary to predetermine the characteristics when creating the optimal driving model. There is this. At this time, an embodiment of the present invention has the advantage of minimizing performance dependence according to the quality and amount of data because it simulates and learns a previously generated virtual physics system.

As input data for deep learning, one or more of vehicle driving and driving information, driving road traffic information, and driving road attribute information obtained by the simulation result may be applied.

At this time, the vehicle operation and driving information includes at least one of the driving date and time, driving section, driving position, speed and real-time fuel efficiency data information, and the driving road traffic information includes at least one of traffic volume and average speed information, and driving road properties The information may include one or more of slope, curvature, speed limit, road type, entry and exit section, and branch section information.

The optimal driving model generated through the in-depth learning may satisfy a case where the difference between the target fuel efficiency and the fuel efficiency for each driving condition is minimal and the difference between the target safety and the safety degree for each driving condition is minimal.

Next, the contents of generating the optimal driving model by learning the simulation result of the virtual physics system using the in-depth strong learning are described as follows.

The smart driving agent method according to an embodiment of the present invention can further improve the performance of the optimal driving model through deep learning by applying reinforcement learning among machine learning methods to a deep neural network structure. have.

That is, the optimal driving model generated through supervised learning during in-depth learning can continuously improve its performance through repeated reinforcement learning in a virtual physics system environment.

At this time, as an in-depth strong learning algorithm, a strategy-based, policy-based, or a combination of these methods can be applied.

After the optimal driving model is generated as described above, the generated optimal driving model is provided as a vehicle (S140), and the vehicle receives it and controls the corresponding vehicle.

The optimal driving model generated in this way can be applied to the cruise system that maintains only a simple set speed being used in the existing vehicle.

In addition, a driving guide through an optimal driving model can be provided to a driver of a current vehicle in a method such as visual, auditory, and tactile senses, and feedback information corresponding to the driving guide can be received from the driver.

In the above description, steps S110 to S140 may be further divided into additional steps or combined into fewer steps, according to an embodiment of the present invention. In addition, some steps may be omitted if necessary, and the order between the steps may be changed. In addition, even if omitted, the contents already described with respect to the smart driving agent system 100 in FIGS. 1 to 2 also apply to the smart driving agent method in FIG. 3.

One embodiment of the present invention may also be embodied in the form of a computer program stored on a medium executed by a computer or a recording medium including instructions executable by a computer. Computer readable media can be any available media that can be accessed by a computer, and includes both volatile and nonvolatile media, removable and non-removable media. In addition, computer readable media may include both computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Communication media typically includes computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, or other transport mechanism, and includes any information delivery media.

Although the methods and systems of the present invention have been described in connection with specific embodiments, some or all of their components or operations may be implemented using a computer system having a general purpose hardware architecture.

The above description of the present invention is for illustration only, and those skilled in the art to which the present invention pertains can understand that the present invention can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

The scope of the present invention is indicated by the following claims rather than the above detailed description, and it should be interpreted that all changes or modified forms derived from the meaning and scope of the claims and equivalent concepts thereof are included in the scope of the present invention. do.

100: smart driving agent system
110: memory
120: processor
130: communication module

Claims (11)

In the smart driving agent system based on the vehicle driving information big data and virtual physics system,
Memory that contains safety information and fuel efficiency information, and a program for generating an optimal driving model for controlling the vehicle.
It includes a processor for executing a program stored in the memory,
As the processor executes the program, virtual physics including a vehicle virtual model and a driving model virtual model based on driving information big data collected from the vehicle or a vehicle of the same model as the vehicle and characteristic data of the vehicle. Create a system, use the results of the simulation of the virtual physics system as learning data, learn artificial intelligence, and generate an optimal driving model for the vehicle,
The driving information big data is a smart driving agent system that includes one or more of driving and driving information of the vehicle, driving road traffic information, and driving road attribute information. According to claim 1,
The processor generates a virtual model of the driving road based on the property information of the driving road, generates a virtual model of the vehicle based on the characteristic data of the vehicle, and generates a virtual model of the vehicle on the road configured through the virtual model of the driving road. Smart driving agent system to create the virtual physics system by configuring the vehicle according to. According to claim 2,
The processor is a primary driving model including the fuel efficiency information on a road constructed through the virtual model of the driving road through learning based on the artificial intelligence learning algorithm to simulate a result of simulating the virtual physical system for driving of the vehicle. Smart driving agent system that will generate. The method of claim 3,
The processor performs a result of performing the simulation on driving of a surrounding vehicle based on an average vehicle speed for each road section extracted from the driving information big data with respect to the primary driving model through learning based on the artificial intelligence learning algorithm, Smart driving agent system that is created as a secondary driving model. The method of claim 4,
A smart driving agent system that uses the secondary driving model as the optimal driving model. According to claim 1,
The characteristic data includes power train information of the vehicle and structural information of the vehicle. According to claim 1,
The driving and driving information of the vehicle includes at least one of driving date, driving section, driving position, speed, and real-time fuel efficiency data information, and the driving road traffic information includes at least one of traffic volume and average speed information, and the driving The road property information includes one or more of slope, curvature, speed limit, road type, entry and exit section and branch section information. According to claim 1,
The processor obtains the driving speed information of the vehicle and matches the position information of the vehicle with a 3D road map to determine road slope, curvature, speed limit, type, entry and exit section, and road section information including branch section information. After obtaining the driving position information of the smart driving agent system to learn the fuel economy information based on the driving speed information corresponding to the driving position information of the vehicle. The method of claim 8,
The processor is a smart driving agent system for acquiring driving speed information of the vehicle including current driving speed information of the vehicle, speed limit information of the driving road of the vehicle, and average speed information of surrounding vehicles. According to claim 1,
The processor generates the optimum driving model that satisfies a case in which the difference between the target fuel efficiency and the fuel efficiency for each driving condition is minimal and the difference between the target safety and the safety degree for each driving condition is minimal based on the driving information big data. Smart driving agent system. Smart driving agency based on vehicle operation information big data and virtual physics system
In the way,
Generating a virtual physical system including a vehicle virtual model and a road virtual model based on the driving information big data collected from the vehicle or a vehicle of the same model as the vehicle and characteristic data of the vehicle;
Simulating the virtual physics system;
Using the simulation result as learning data to perform artificial intelligence learning and thereby generating an optimal driving model for the vehicle;
Providing the optimal driving model to the vehicle,
The driving information big data includes one or more of driving and driving information of the vehicle, driving road traffic information, and driving road attribute information,
The characteristic data includes power train information of the vehicle and structural information of the vehicle.

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