Driving information display apparatus, driving information display method, management server, and storage medium

Abstract

The invention relates to a driving information display apparatus and method, a management server, and a storage medium. In a driving information display apparatus and method for providing guidance for personalizing a driving route of an electric vehicle, the driving information display apparatus may include a processor configured to perform control to receive driving guidance information and position information of the electric vehicle and output a guidance screen corresponding to the driving guidance information; and a storage unit configured to store the road information and the algorithm executed by the processor. The driving guidance information may include route information including a path via the charging station, the route information is generated by deriving a driving type based on driving information including a destination of the electric vehicle, deriving a weight for each charging factor based on the driving information, and using the derived driving type and the derived weight for each charging factor.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Korean Patent Application No. 10-2023-0175379 filed on Dec. 6, 2023, which is hereby incorporated by reference in its entirety for all purposes. Technical Field

[0003] The present disclosure relates to a driving information display device and method for providing guidance for a personalized electric vehicle driving route. Background Art

[0004] In terms of the characteristics of electric vehicles, electric vehicles may be difficult to drive comfortably due to long charging time and insufficient number of charging stations. Therefore, there is a need for a route guidance service that considers the battery status of the electric vehicle and guides the electric vehicle to a charging station.

[0005] In response to this demand, a technology for changing a guided route by taking into account the remaining battery charge level at each location when guiding an electric vehicle through a guided route is disclosed, such as a technology named “Navigation System for Electric Vehicle” disclosed in U.S. Patent No. 9170118. In this technology, in order to ensure the next driving safety when the electric vehicle arrives at the destination, a target remaining battery charge level at the destination is set, and route guidance for driving via charging stations is provided to achieve the target remaining battery charge level.

[0006] However, when a route is set so that only the drive to the destination can be completed safely, inconvenience may be experienced during the next drive after reaching the destination due to insufficient battery charge level. Therefore, a route guidance technology that manages the battery status by considering the next drive is needed.

[0007] The information included in the background of the disclosure is only for enhancement of understanding of the general background of the disclosure and should not be taken as an acknowledgement or any form of suggestion that this information forms the technology already known, available or used in the art. Summary of the invention

[0008] The present disclosure relates to a driving information display device and method for providing guidance for a personalized electric vehicle driving route, and more specifically, to a driving information display device for providing driving guidance information including route information including a path passing through an electric vehicle charging station based on a driver's driving pattern or schedule, and a method for operating the driving information display device.

[0009] An embodiment of the present disclosure is to provide driving guide information that enables comfortable driving by considering the remaining battery charge level of an electric vehicle.

[0010] An embodiment of the present disclosure is to improve the overall convenience of driving of an electric vehicle by predicting not only driving to a destination but also driving after reaching the destination.

[0011] An embodiment of the present disclosure is to predict a driving type as to whether current driving is round-trip driving or one-way driving by analyzing a driving pattern of each driver, and to provide route guidance suitable for the driving type.

[0012] An embodiment of the present disclosure is to provide customized route guidance according to circumstances such as the driver's next schedule even when the driver drives the same route.

[0013] Problems to be solved as exemplary embodiments of the present disclosure are not limited to the above-described problems, and solutions to other problems of embodiments of the present disclosure may be clearly understood by those skilled in the art from the following detailed description of the present disclosure.

[0014] According to various aspects of the present disclosure, a driving information display device may include: a processor configured to perform control to receive driving guidance information and position information of an electric vehicle and output a guidance screen corresponding to the driving guidance information; and a storage unit configured to store road information and an algorithm executed by the processor; and the driving guidance information includes route information, the route information includes a path via a charging station, and the route information is generated by deriving a driving type based on driving information including a destination of the electric vehicle, deriving a weight for each charging factor based on the driving information, and using the derived driving type and the derived weight for each charging factor.

[0015] The route information may be derived by determining a remaining charge level at a destination based on the derived driving type and a derived weight for each charging factor, and including a path via a charging station to meet the determined remaining charge level at the destination.

[0016] The remaining charge level at the destination may be determined by predicting the next drive after the current drive based on the driving type and reflecting information related to the predicted next drive in the determination.

[0017] The remaining charge level at the destination may be determined such that a minimum remaining charge level is maintained even after additional driving from the destination to the nearest charging station.

[0018] The remaining charge level at the destination may be determined so that, when the driving type is round-trip driving, a minimum remaining charge level is maintained even after the return driving is completed.

[0019] The driving type can be derived by inputting the driving information of the electric vehicle into a personalized driving type artificial intelligence model, which is generated by performing training using past driving information of the driver of the electric vehicle as training data.

[0020] The weight for each charging factor may be derived by inputting driving information of the electric vehicle into a personalized charging factor artificial intelligence model generated by performing training using past driving information of the driver of the electric vehicle as training data.

[0021] According to various aspects of the present disclosure, a driving information display device includes a driving information management server equipped with a central processing unit and a memory, and the driving information management server includes: a connection establishment unit, configured to establish a connection to exchange information with driving information display devices of multiple electric vehicles; a driving type derivation unit, configured to derive a driving type based on driving information including a destination of each electric vehicle received from the electric vehicle; a per-charging factor weight derivation unit, configured to derive a weight for each charging factor based on the driving information; a route information generation unit, configured to generate route information including a path through a charging station generated using the derived driving type and the derived weight for each charging factor; and a driving guidance information transmission unit, configured to transmit driving guidance information including the generated route information to the electric vehicle.

[0022] The methods and apparatus of the present disclosure have other features and advantages that will be apparent from or set forth in more detail in the accompanying drawings and the following detailed description incorporated herein, which together serve to explain certain principles of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a block diagram showing an internal configuration of a driving information display device according to various exemplary embodiments of the present disclosure;

[0024] Figure 2 is a block diagram showing an internal configuration of a driving information management server according to various exemplary embodiments of the present disclosure;

[0025] Figure 3 is a diagram illustrating an example of determining a remaining charge level at a destination according to a driving type in a driving information display device according to various exemplary embodiments of the present disclosure;

[0026] Figure 4is a diagram showing an example of route guidance that varies depending on the presence / absence of a charging station close to a destination in a driving information display device according to various exemplary embodiments of the present disclosure;

[0027] Figure 5 is a diagram showing an example of route guidance according to scene changes during round-trip commuting driving in a driving information display device according to various exemplary embodiments of the present disclosure;

[0028] Figure 6 is a diagram showing an example of route guidance that varies depending on the presence / absence of a slow charger at a destination in a driving information display device according to various exemplary embodiments of the present disclosure;

[0029] Figure 7 is a diagram showing an example of route guidance that changes depending on the presence / absence of a schedule after reaching a destination in a driving information display device according to various exemplary embodiments of the present disclosure;

[0030] Figure 8 is a diagram showing a process flow of generating a route guidance by classifying a driving type and obtaining a weight for each charging factor in a driving information display device according to various exemplary embodiments of the present disclosure; and

[0031] Fig. 9 is a flowchart illustrating the flow of a driving information display method according to various exemplary embodiments of the present disclosure.

[0032] It will be appreciated that the drawings are not necessarily drawn to scale, and that the drawings present somewhat simplified representations of various features illustrating the basic principles of exemplary embodiments of the present disclosure. The specific design features of exemplary embodiments of the present disclosure as included herein (including, for example, specific dimensions, orientations, locations, and shapes) will be determined in part by the specific intended application and use environment.

[0033] In the drawings, reference numbers refer to the same or equivalent parts of exemplary embodiments of the present disclosure throughout the several figures of the drawing. DETAILED DESCRIPTION

[0034] Reference will now be made in detail to various embodiments of the present disclosure, examples of which are shown in the accompanying drawings and described below. Although the present disclosure will be described in conjunction with exemplary embodiments of the present disclosure, it should be understood that this specification is not intended to limit the present disclosure to those exemplary embodiments of the present disclosure. On the other hand, the present disclosure is intended to cover not only the exemplary embodiments of the present disclosure, but also various replacements, modifications, equivalents and other embodiments that may be included within the spirit and scope of the present disclosure as defined by the appended claims.

[0035] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. In the following description of the present disclosure, when it is determined that the detailed description of any related known configuration or function may obscure the subject matter of the present disclosure, the detailed description will be omitted. In addition, in the following description of the exemplary embodiments of the present disclosure, specific numerical values ​​are only examples, and the scope of the present disclosure is not limited by the specific numerical values.

[0036] In the following description of the components of the exemplary embodiments of the present disclosure, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only used to distinguish the corresponding components from other components, and the properties, sequential positions and / or orders of the corresponding components are not limited by these terms. In addition, unless otherwise defined, all terms (including technical or scientific terms) used in this document include the same meanings as those commonly understood by technicians in the field to which the exemplary embodiments of the present disclosure belong. Terms such as those defined in commonly used dictionaries should be interpreted as having meanings consistent with the meanings in the context of the relevant technology, and should not be interpreted as having ideal or overly formal meanings unless clearly defined in this application.

[0037] The following will refer to Figures 1 to 9 Exemplary embodiments of the present disclosure are described in detail.

[0038] Figure 1 is a block diagram showing an internal configuration of the driving information display device 101 according to various exemplary embodiments of the present disclosure.

[0039] The driving information display device 101 according to the exemplary embodiment may be provided inside a transportation system such as a vehicle, or may be implemented in a detachable form. The driving information display device 101 may generally include a vehicle navigation system, an audio, video and navigation (AVN) system, a head-up display (HUD), etc., and may be implemented in a form in which an application is provided on a mobile phone terminal such as a smart phone.

[0040] The driving information display device 101 according to the exemplary embodiment may exist in the form of a server outside a transportation system (such as a vehicle). The driving information display device 101 may be implemented to generate driving guidance information by processing determination while existing outside the transportation system, and output the driving guidance information to a display existing inside the transportation system. In addition, different embodiments may be implemented. The scope of the rights of the present disclosure is not limited by the forms of these embodiments.

[0041] Furthermore, the driving information display device 101 of the exemplary embodiment may operate in conjunction with equipment for autonomous driving control such as an advanced driver assistance system (ADAS), a smart cruise control (SCC) system, a forward collision warning (FCW) system, and the like.

[0042] As shown in the drawing, a driving information display device 101 according to an exemplary embodiment may include a processor 110 , a storage unit 120 , a communication unit 130 , and an output unit 140 .

[0043] The processor 110 is configured to control the storage unit 120 , the communication unit 130 , and the output unit 140 to execute an application, process data according to an algorithm defined in the application, communicate with an external module, and provide a processing result to a user.

[0044] The processor 110 may refer to a chip for processing general algorithms (such as a central processing unit (CPU) or an application processor (AP)) or a collection of these chips. The processor 110 may refer to a chip optimized for floating-point arithmetic to process artificial intelligence algorithms such as deep learning (such as general-purpose computing on a general processing unit (GPGPU)) or a collection of these chips. Alternatively, the processor 110 may refer to a module in which various types of chips execute algorithms and process data in a connected and distributed manner.

[0045] The processor 110 may be electrically connected to the storage unit 120 (storage medium) and the communication unit 130, may electrically control independent components, may be a circuit that executes software commands, and may perform various types of data processing and determination described later. The processor 110 may be, for example, an electronic control unit (ECU), a microcontroller unit (MCU), or another low-level controller installed on the transportation system.

[0046] The storage unit 120 may be a storage medium that stores road information and an algorithm executed by a processor. The road information may include map information, road traffic condition information, etc. Depending on the configuration of the driving information display device 101 of the present disclosure, the form or amount of road information stored in the driving information display device 101 may vary.

[0047] In some cases, the storage unit 120 may store road information including map information and traffic condition information of all serviceable areas and provide services based on the road information. Alternatively, the storage unit 120 may only temporarily store road information related to the location for guidance and provide services based on the temporarily stored road information.

[0048] Depending on the form of the driving information display device 101 according to the exemplary embodiment of the present disclosure implemented inside or outside the transportation system, the communication method used, the storage space of the storage unit 120, and / or the input / output speed, this can be implemented in different forms. This is a part that can be independently selected by those skilled in the art according to the implementation situation. The scope of rights of the present disclosure is not limited by such changes in the implementation.

[0049] The road information stored in the storage unit 120 may include not only general road information but also information for providing guidance regarding an entrance, an exit, a parking position, etc. within an indoor area such as an underground parking lot.

[0050] In addition, the road information stored in the storage unit 120 may include various types of display information displayed in the guidance information. The display information may include various types of information contained in the guidance information displayed in the driving scene, such as intersections, traffic lights, sidewalks, destinations, and major landmarks. In addition, the display information included in the road information may include parking locations, entrance locations, ramps for moving between floors, indoor facilities, road information outside the indoor area connected to the exit, etc., for the purpose of guiding inside the indoor area. Such display information may each be composed of a combination of the name of the display information displayed as the guidance information and information related to the location where the corresponding display information is displayed.

[0051] The storage unit 120 may have various forms and may be at least one type of storage medium, such as a flash memory, a hard disk, a micro, a card (e.g., a secure digital (SD) card), an extreme digital (XD) card, a random access memory (RAM), a static RAM (SRAM), a read-only memory (ROM), a programmable ROM (PROM), an electrically erasable PROM (EPROM), a magnetic memory (MRAM), a magnetic disk or optical disk type storage medium, etc. or any combination thereof. Different types of storage media or combinations of different types of storage media may be selected depending on the amount of data to be stored, processing speed, storage time, etc.

[0052] The algorithm stored in the storage unit 120 may be implemented as an executable computer program and may be implemented to be stored in the storage unit 120 and then executed in a desired scenario. The algorithm stored in the storage unit 120 may be interpreted as including an instruction form that is temporarily loaded into a volatile memory and instructs the processor to perform a specific operation.

[0053] The communication unit 130 receives information for driving guidance from outside the driving information display device 101 of the present disclosure through a wired / wireless communication network, and transmits necessary information to an external module.

[0054] The communication unit 130 may receive road information stored in the storage unit 120, an algorithm executed by the processor 110, etc. from an external module, and may transmit information related to the current state of the transportation system to the outside to obtain necessary information related to the transmitted information. For example, the communication unit 130 may continuously receive traffic information from a traffic information server to check real-time traffic information, and may be configured to transmit the location and route information of the transportation system found by a module such as a global positioning system (GPS) receiver to the outside to obtain real-time traffic information of an area related to the location and route of the transportation system.

[0055] The communication unit 130 is a hardware device implemented using various electronic circuits to transmit and receive signals through wireless or wired connections. In an exemplary embodiment of the present disclosure, the communication unit 130 can perform communication within the transportation system using the basic transportation network communication technology, and can perform vehicle-to-infrastructure (V2I) communication with a server, infrastructure, another transportation system, etc. outside the transportation system using wireless Internet access or short-range communication technology. In this case, the communication within the transportation system can be performed using controller area network (CAN) communication, local interconnect network (LIN) communication, FlexRay communication, etc. as the basic transportation network communication technology. In addition, such wireless communication technology may include wireless LAN (WLAN), wireless broadband (WiBro), Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), etc. In addition, short-range communication technology may include Bluetooth, ZigBee, ultra-wideband (UWB), radio frequency identification (RFID), infrared data association (IrDA), etc.

[0056] The output unit 140 may output augmented reality information controlled by executing an algorithm stored in the storage unit 120 by the processor 110. Augmented reality is a technology that enables providing relevant information by adding graphic information to an image or scene of the real world.

[0057] The output unit 140 may be implemented as a head-up display (HUD), a cluster, an audio, video and navigation (AVN) system, a human-machine interface (HMI), etc. In addition, the output unit 140 may include at least one of a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, an active matrix OLED (AMOLED) display, a flexible display, a curved display, and a three-dimensional (3D) display. Some of these displays may be implemented as a transparent display configured in a transparent or translucent form to enable viewing of the outside of the transparent display. In addition, the output unit 140 may be configured as a touch screen including a touch panel, and may be used as an input device as well as an output device.

[0058] In the present disclosure, a vehicle may be described as being based on a concept including various transportation systems. In some cases, a vehicle may be interpreted as being based on a concept including not only various land transportation vehicles (such as cars, motorcycles, trucks, and buses) driving on roads but also various transportation systems such as airplanes, drones, ships, etc.

[0059] Therefore, in the present disclosure, the electric vehicle may be interpreted as a concept based on various transportation systems including storing electric energy in a secondary battery and using the secondary battery as a power source in the various transportation systems.

[0060] The driving information display device 101 according to the present disclosure may have different exemplary embodiments depending on the driving guide information processed by the processor 110. Therefore, the generation of the driving guide information received by the processor 110 and the information included in the driving guide information will be described below by way of example.

[0061] As described above, the processor 110 performs control to receive the driving guidance information and the information related to the position of the electric vehicle and outputs a guidance screen corresponding to the driving guidance information. The driving guidance information may include various types of information. When the driver sets a destination and wants to receive guidance information related to the destination through a screen, the driving guidance information may include information related to the destination and the route to the destination. Various types of other information for driving guidance of the vehicle may be included in the driving guidance information.

[0062] Specifically, in the case of an electric vehicle, the driving distance is short, charging takes a long time, and there are not enough charging stations, so it is important to provide guidance on the best route passing through the charging station during route guidance. Therefore, the processor 110 can be configured to provide guidance on the best route along which the driver can safely drive to the destination when charging the electric vehicle at a necessary point by using the electric vehicle charging station information included in the map information stored in the storage unit 120.

[0063] The driving guide information may be information received from the driving information management server 102 through the communication unit 130, or may be information derived by the processor 110 through the processor's own computational processing. The driving guide information includes route information that can provide guidance as to whether the driver will pass by a charging station when driving the electric vehicle to a destination, and provide guidance as to the charging station the driver will pass by when he or she will pass by the charging station.

[0064] When such route information is included in the driving guide information, the processor 110 performs control to display the route on the map information stored in the storage unit 120 and output the map information through the output unit 140 .

[0065] The driving guidance information includes route information including a path via a charging station, the route information being generated by deriving a driving type based on driving information including a destination of the electric vehicle, deriving a weight for each charging factor based on the driving information, and using the derived driving type and the derived weight for each charging factor.

[0066] The driving type may indicate the purpose and environment in which the electric vehicle is driven to the current destination of the electric vehicle. For example, the driving type may be basically classified as one-way driving or round trip driving. In addition, one-way driving may be classified as short-distance driving, medium-distance driving, or long-distance driving according to the driving distance. Medium-distance driving may be defined as driving for a same-day driving schedule, and long-distance driving may be defined as driving for a one-night or multi-night driving schedule.

[0067] In the case where the driving type can be accurately identified when the driver starts driving by inputting a destination into the electric vehicle, different route guidance can be provided depending on the driving type. For example, when the destination input by the driver is determined to correspond to a round-trip driving type, route guidance can be provided that allows the driver to maintain an appropriate remaining charge level when returning to the starting point by considering a route from the starting point through the destination to the starting point.

[0068] When providing a route by considering only the driving from the starting point to the destination, providing guidance to allow the electric vehicle to maintain an appropriate remaining battery charge level when it arrives at the destination. Therefore, it is difficult to provide route guidance that takes the next driving into consideration.

[0069] For example, in a case where the minimum remaining charge level is 30% of the total battery capacity, when the battery charge level is expected to be 20% upon arrival at a destination input by the driver, guidance will be provided to drive to a charging station, charge the electric vehicle with power corresponding to 10% of the total battery capacity, and then move to meet the minimum remaining charge level.

[0070] However, in the case of round trip driving in which the electric vehicle reaches the destination and immediately returns to the starting point after a short period of time, the return driving starts at 30% of the total battery capacity, so that the electric vehicle needs to visit the charging station and charge with electricity again. There is the inconvenience of having to pass the charging station twice during the round trip.

[0071] In a case where it can be predicted that the current driving is a round trip driving, when a charge level sufficient to safely complete the return driving can be predicted and the driver is guided to visit a charging station and charge the electric vehicle to the predicted charge level, driving can be performed to meet the minimum remaining charge level when returning to the starting point after completing the round trip by stopping only once at the charging station.

[0072] The driving type can be configured to be derived by inputting the driving information of the electric vehicle into a personalized artificial intelligence model, which is generated by performing training using the past driving information of the driver of the electric vehicle as training data. Past driving information may include information related to the starting point, destination, driving distance, departure day, departure time span, etc. In other words, each time the driver drives the electric vehicle, the driving information is accumulated in the database, and information related to the driving type (such as round-trip driving, long-distance one-way driving, or short-distance one-way driving) is stored together with the driving information, and a personalized driving type artificial intelligence model can be generated by performing training using these types of information as training data.

[0073] For example, in a case where a driver starts driving on Monday morning with home as the starting point and work as the destination and the driving distance is 5 km, when the next driving performed immediately after the current driving is driving with work as the starting point and home as the destination on the same evening, it is determined that the driving type of the morning driving is round-trip driving, specifically travel driving, and the driving type is stored in the database together with the driving information.

[0074] When such data is accumulated over a long period of time, sufficient data is accumulated in the database to train a personalized driving artificial intelligence model, which is able to predict the driving type based on driving information (such as the user's starting point, destination, driving distance, departure day, and time span).

[0075] Various artificial intelligence algorithms can be used to generate a personalized driving artificial intelligence model. Regression algorithms such as random forest regressors and neural networks such as deep learning can be used. The present disclosure is not limited to a specific artificial intelligence algorithm.

[0076] When the driving type can be predicted using a personalized driving-type AI model, more efficient route guidance can be provided based on the predicted driving type.

[0077] Meanwhile, the weight for each charging factor may include a weight for a charging type (or charging speed) indicating whether the charging in question is fast charging or slow charging, a weight for charging time, and a weight for a charging location (starting point, destination, or waypoint). For example, when it is determined that it is desired to charge the electric vehicle as much as possible at the starting point for a long period of time using fast charging, and then set off by considering the current driving scenario, a higher weight is assigned to fast charging, the charging time is set to a longer value, and a higher weight is assigned to the starting location as the charging location.

[0078] When charging information suitable for the current scenario and the driver's tendency is generated using the weight for each charging factor in this manner, route guidance can be generated based on the charging information.

[0079] The weight for each charging factor can be derived by inputting the driving information of the electric vehicle into a personalized charging factor artificial intelligence model, which is generated by performing training using the past driving information of the driver of the electric vehicle as training data. In other words, when driving information such as the starting point, destination, driving distance, departure day, departure time span, etc. is organized into a database and information related to the charging location (starting point, destination, or waypoint), charging speed, and charging time during the corresponding driving period is stored in association with the corresponding driving information fragment, a personalized charging factor artificial intelligence model can be generated, which can use the current driving information as an input value to predict a charging method suitable for the driver's tendency during the current driving period.

[0080] When the driving type and the weight for each charging factor are determined in this manner, the remaining charge level at the destination is determined using the driving type and the weight for each charging factor derived as described above, and route information including a path via a charging station is generated to meet the determined remaining charge level at the destination.

[0081] The operation of determining the driving type and the weight for each charging factor and generating route information in this manner can be performed in the driving information management server 201 connected to the driving information display device 101 of the present disclosure through a communication network. The driving information display device 101 receives the generated route information and provides the route information to the driver as driving guide information.

[0082] The remaining charge level at the destination is determined by predicting the next drive after the current drive based on the driving type and reflecting information related to the predicted next drive. In other words, in the case where the driving type is a round trip type, route guidance is generated so that the remaining charge level can be maintained above the minimum charge level even after driving along the entire route including the return drive. In the case of a one-way type, route information can be generated by taking into account the situation of using the next charging station.

[0083] In addition, when the driving type is a one-way type, the remaining charge level at the destination can be determined so that the minimum remaining charge level can be maintained even after additional driving from the destination to the nearest charging station. In other words, the route can be set so that when the electric vehicle needs to be charged for the next driving after reaching the destination, the driver can safely reach the next charging station.

[0084] When the next destination can be predicted after reaching the destination, the route can be set so that the minimum remaining charge level is maintained after moving to the nearest charging station on the route to the next destination.

[0085] When a slow charger or a fast charger is installed at a destination, charging can be performed before starting the next drive from the destination, so that route guidance can be provided to keep the remaining charge level at the destination low.

[0086] The driving route may include information related to the charging station and the amount of electricity used for charging. When the charging time at the charging station located at the waypoint increases, the problem of increasing the total driving time arises. Therefore, there is a need for route guidance that minimizes the charging time at the charging station located at the waypoint and satisfies the remaining charge level at the destination.

[0087] Therefore, it is desirable to generate a database in which information is organized regarding whether a charging station exists at a destination and whether the charging station is fast or slow, and to use the database when generating a driving route.

[0088] In addition, the remaining charge level at the destination may be determined so that when the driving type is round trip driving, the minimum remaining charge level is maintained even after the return driving has been completed. In the case of round trip driving, when sufficient charging is performed before departure or at a waypoint so that the minimum remaining charge level is maintained even after returning to the starting point, the inconvenience of having to pass by a charging station again due to a low charge level can be avoided during the return driving.

[0089] Furthermore, in round-trip driving, when a charger is present at the destination and when information indicating whether the electric vehicle will stay at the destination long enough to utilize electric power for charging is available, the battery state of the electric vehicle when the return drive begins can be predicted and more efficient route guidance can be generated.

[0090] Regarding information related to the time period that the driver will stay at the destination during round-trip driving, when the time the driver stays at the destination in the case of round-trip driving for each driving information segment is recorded in a database that accumulates past driving information as described above, an artificial intelligence model can be generated based on the above database to predict the time spent at the destination when the driving information is input.

[0091] For example, when a driver uses an electric vehicle to commute to work, there is a time equivalent to the length of work hours between driving out to work and driving back from work. When learning a database in which this type of time information has been accumulated for a long time, it is possible to predict how long the driver will stay at his or her destination when he or she goes to work in the morning. When the driver is expected to stay at the destination for eight hours and there is a slow charger at the destination, the amount of electricity charged to the electric vehicle using the slow charger during his or her stay at the destination can be calculated. When this amount of electricity is calculated as the starting capacity for the return drive, more accurate route information can be generated.

[0092] In addition, when the driver performs an operation in conjunction with the personal schedule database 202, the driving type can be identified, and the next driving can be predicted by considering the driver's schedule. For example, when the driver sets the destination to the workplace in the morning, if there is no separate schedule, the schedule can be identified as round-trip driving between home and the workplace. When a schedule for driving to another location after work hours is stored in the personal schedule database, it can be determined that it is a one-way schedule rather than a round-trip schedule.

[0093] Furthermore, when a location is set in the stored schedule, the remaining charge level at the destination is set by taking into account the movement to the location, through which efficient route guidance can be provided. In other words, it is possible to determine that the driving type is one-way driving and set the remaining charge level at the destination to a value sufficient to maintain the minimum remaining charge level when moving to the nearest charging station on the route to the location stored in the schedule. It is also possible to determine the remaining charge level at the destination so that the minimum remaining charge level can be maintained when moving to the location stored in the schedule without driving via a charging station.

[0094] Figure 2 is a block diagram illustrating an internal configuration of a driving information management server according to various exemplary embodiments of the present disclosure.

[0095] The driving information management server 201 according to various exemplary embodiments of the present disclosure is a server equipped with a central processing unit (CPU) and a memory, and may include a stand-alone server or a cloud. The driving information management server 201 is not limited to a specific configuration as long as the driving information management server 201 is able to obtain the target remaining battery charge level by processing information received from the above-mentioned driving information display device 101 through a wired or wireless communication network, and transmit the target remaining battery charge level back to the driving information display device 101 through the communication network.

[0096] The driving information management server 201 of the exemplary embodiment of the present disclosure processes the calculations required to generate the charging guidance information displayed on the driving information display device 101. Therefore, the process of generating the charging guidance information in the above-mentioned driving information display device 101 can be performed in the driving information management server 201. Therefore, the description of the process of generating the charging guidance information displayed on the driving information display device 101 can be applied to the driving information management server 201 without significant changes, and vice versa.

[0097] As shown in the figure, the driving information management server 201 may include a connection setting unit 210, a driving type derivation unit 220, a per-charging factor weight derivation unit 230, a route information generation unit 240, and a driving guide information transmission unit 250. The independent components may be configured as software components running within the above-mentioned server device, and various modifications may be made.

[0098] The connection establishing unit 210 establishes a connection to exchange information with the driving information display devices 101 of a plurality of electric vehicles. The connection establishing unit 210 establishes a connection with the communication unit 130 of the driving information display device 101 through a wired or wireless communication network and is not limited to a specific type of communication.

[0099] The driving type derivation unit 220 may derive the driving type based on driving information including a destination of the electric vehicle received from the electric vehicle.

[0100] As described above, the driving type may indicate the purpose and environment in which the electric vehicle is driven to the current destination of the electric vehicle. For example, the driving type may be basically classified as one-way driving or round-trip driving. In addition, one-way driving may be classified as short-distance driving, medium-distance driving, or long-distance driving, depending on the driving distance. Medium-distance driving may be defined as driving for a same-day driving schedule, and long-distance driving may be defined as driving for a one-night or multi-night driving schedule.

[0101] In the case where the driving type can be accurately identified when the driver starts driving by inputting a destination into the electric vehicle, different route guidance can be provided depending on the driving type. For example, when the destination input by the driver is determined to correspond to a round-trip driving type, route guidance can be provided that allows the driver to maintain an appropriate remaining charge level when returning to the starting point by considering a route from the starting point through the destination to the starting point.

[0102] The driving type derivation unit 220 accumulates driving related information (such as the starting point, destination, driving distance, departure day, and departure time span) and surrounding scene information (such as weather) in the past driving information database 203 for each driving, determines the driving type by checking the next driving information after the driving, and then stores the information related to the driving type of the driving. When the driving type of the corresponding driving is stored in the past driving information database 203 together with the driving related information and the surrounding scene information in this way, and an artificial intelligence model is generated by performing training using the accumulated data as training data, a personalized driving type artificial intelligence model can be generated.

[0103] The driving type derivation unit 220 obtains a prediction result for the driving type by inputting the driving information of the electric vehicle into the personalized driving type artificial intelligence model generated in this way.

[0104] In addition, when operating in conjunction with the driver's personal schedule database 202, the driving type derivation unit 220 can be trained based on information related to the driver's schedule before and after driving and information related to the driving position for each schedule, and can predict the driving type by taking the driver's schedule into consideration by using information related to the driver's schedule during the current driving as input data.

[0105] For example, even when the driver drives to work in the morning as usual, a case where the driver has plans to go somewhere else after work and a case where the driver returns home immediately are distinguished from each other through personal schedule information. Depending on the type or location of the schedule, it is predicted whether the driver will stay with the vehicle or leave the vehicle for a while for the schedule and then return home. Therefore, more efficient route guidance can be provided.

[0106] This prediction of the driving type is based on the driver's personalized habits, so that the prediction can improve accuracy, but may not have 100% accuracy. Therefore, the driving information display device 101 of the present disclosure can provide the predicted driving type to the driver, can receive confirmation from the driver, and can generate more accurate route guidance.

[0107] The weight derivation unit 230 for each charging factor may derive a weight for each charging factor based on driving information. The weight for each charging factor may include a weight for a charging type (or charging speed) indicating whether the charging in question is fast charging or slow charging, a weight for charging time, and a weight for a charging location (starting point, destination, or waypoint). For example, when it is determined that it is desired to charge the electric vehicle as much as possible at the starting point for a long period of time using fast charging, and then set off by considering the current driving scenario, a higher weight is assigned to fast charging, the charging time is set to a longer value, and a higher weight is assigned to the starting location as the charging location.

[0108] When charging information suitable for the current scenario and the driver's tendency is generated using the weight for each charging factor in this manner, route guidance can be generated based on the charging information.

[0109] The weight derivation unit 230 for each charging factor can derive the weight for each charging factor by inputting the driving information of the electric vehicle into the personalized charging factor artificial intelligence model, which is generated by performing training using the past driving information of the driver of the electric vehicle as training data. As described above, various types of information such as driving information and surrounding scene information during past driving are accumulated and stored in the past driving information database 203. Information related to how the driver charges the electric vehicle at the starting point, destination, or waypoint for each driving is also stored.

[0110] In this way, information related to the charging factors for the corresponding driving can be stored together with the driving information and the surrounding scene information, and the accumulated data can be used as training data to generate a personalized charging factor artificial intelligence model. When the current driving information and the surrounding information are input into the generated personalized charging factor artificial intelligence model, the weights for the charging factors during the current driving can be predicted, and these weights can be used to provide route guidance.

[0111] In addition, when the personalized charging factor artificial intelligence model is trained and operated in conjunction with the personal schedule database 202, the driver's schedule before or after driving can be set as a parameter. The driver's charging type can be changed depending on whether the driver has a schedule, so that when the driver's schedule is also taken into account, more accurate customized guidance for each scenario can be provided.

[0112] The personalized driving type artificial intelligence model and the personalized charging factor artificial intelligence model used in the driving type derivation unit 220 and the per-charging factor weight derivation unit 230 are generated in this way using the data accumulated in the personal schedule database 202 and the past driving information database 203 as training data, respectively. Since the individual models are somewhat different from each other in their characteristics, it is necessary to select information suitable for generating each prediction model from the stored information and use the information as training data and input data.

[0113] Various artificial intelligence algorithms can be used to generate personalized driving artificial intelligence models and personalized charging factor artificial intelligence models. Regression algorithms such as random forest regressors and neural networks such as deep learning can be used. The present disclosure is not limited to a specific artificial intelligence algorithm.

[0114] The route information generating unit 240 may generate route information including a path via a charging station, the route information being generated using the derived driving type and the derived weight for each charging factor. When generating route information for an electric vehicle, guidance is provided so that the remaining charge level when arriving at the destination is maintained above a set level. In the past, the target remaining charge level at the destination was set to a specific value in a battery output stable region (plateau) corresponding to a range of 30% to 70% of the remaining battery charge level of the electric vehicle. Guidance on charging at the starting point or charging via a charging station is provided to meet this requirement.

[0115] However, when route guidance is provided according to the same standard regardless of the driving type or the driver's inclination, the driver may feel uncomfortable, especially during the next driving.

[0116] Therefore, the route information generation unit 240 does not simply meet the target remaining charge level at the destination set in the electric vehicle, but uses the derived driving type and the derived weight for each charging factor to decide the target remaining charge level at the destination. In addition, the route information generation unit 240 can generate route information including a path via a charging station to meet the target remaining charge level at the destination.

[0117] This makes it possible to provide route guidance suitable for the driving type and the charging tendency of the driver. Route guidance can be provided that takes into account not only the current driving but also the next driving or future driving. Therefore, the convenience of the driver can be maximized. Specifically, the route guidance provides guidance on the charge level (such as the charge level before departure, the charge level at the charging station during driving, or the charge level at the destination) and various types of guidance such as charging time and charging speed. Therefore, the driver can drive the electric vehicle conveniently.

[0118] The route information generation unit 240 can predict the next drive after the current drive based on the driving type, and determine the remaining charge level at the destination by reflecting the information related to the next drive predicted here. Even when the driving has the same starting point and destination, the route guidance can be completely changed depending on whether the purpose of the driving is round-trip driving or one-way driving. In the case of round-trip driving, it is desirable to provide guidance so that the electric vehicle can return to the original starting point without frequent additional charging. In the case of one-way driving, it is desirable to provide guidance to maintain the charge level to avoid inconvenience during the next driving.

[0119] The route information generation unit 240 determines the remaining charge level at the destination so that the minimum remaining charge level is maintained even after driving additionally from the destination to the nearest charging station. When there is no prediction of the next location to be moved to after arriving at the destination, the determination can be made based on the charging station that is closest in terms of distance. When the location to be moved to after arriving at the destination can be predicted based on information such as the driver's schedule, the determination can be made based on the nearest charging station on the route to the location. When charging is possible at the destination, the electric vehicle can be guided to reach the destination while maintaining the minimum remaining charge level. The minimum remaining charge level can be set to approximately 30% of the total battery capacity so that the range maintains the battery output stable area (platform) as described above. In some cases, the minimum remaining charge level can be set based on the drivable distance.

[0120] When the driving type is round trip driving, the route information generating unit 240 determines the remaining charge level at the destination so that the minimum remaining charge level is maintained even after the return driving. In the case of one-way driving, the minimum remaining charge level can be maintained at the destination, or after reaching the destination until the electric vehicle arrives at the next charging station. In the case of round trip driving, when the minimum remaining charge level can be maintained even after the return driving is completed, the round trip driving can be comfortably performed without charging during driving.

[0121] The driving guidance information transmission unit 250 can transmit the driving guidance information including the generated route information to the electric vehicle. The communication unit 130 of the driving information display device 101 of the electric vehicle passes the driving guidance information transmitted in this way to the processor 110, and the processor 110 performs control so that the output unit 140 outputs the guidance screen corresponding to the driving guidance information.

[0122] In addition, the driving information management server 201 can continuously collect driving-related information (such as starting point, destination, driving distance, departure date and departure time) and information such as charging type (high charging or slow charging), charging location and charging time from the driving information display devices 101 of multiple electric vehicles during charging to maintain the past driving information database 203 and generate training data through the past driving information database.

[0123] Figure 3 is a diagram illustrating an example of determining a remaining charge level at a destination according to a driving type in a driving information display device according to various exemplary embodiments of the present disclosure.

[0124] Figure 3 Case (a) in FIG. 1 shows route guidance when it is determined that the driving type is leisure trip driving included in one-way driving. As shown in the figure, in the case where the driving type is determined to be one-way driving when receiving driving guidance by entering the destination from the starting point, unlike in the past, the minimum remaining charge level is not maintained when arriving at the destination, but the remaining charge level at the destination is determined to be a level that allows safe driving to the nearest charging station where charging can be performed during the next driving after arriving at the destination.

[0125] exist Figure 3 In the case (a) diagram, the nearest charging station is 30 km away when arriving at the destination, and therefore the remaining charge level at the destination is determined to be a charge level that allows the electric vehicle to drive 30 km more than the minimum remaining charge level when arriving at the destination. In order to meet this requirement, the driving information display device 101 can guide the driver to charge the electric vehicle more sufficiently before departure, or can guide the driver to select a charging station that can be conveniently used during driving, stop through the charging state, and charge the electric vehicle.

[0126] When there is a charging station capable of charging the electric vehicle with electricity at the destination, it is sufficient to provide guidance to maintain a charge level equal to or higher than the minimum remaining charge level at the destination. However, when the driver's pattern or schedule information can be searched, it can be determined whether the driver will stay at the destination long enough to charge the electric vehicle and then set off again, and different route guidance can be provided.

[0127] For example, when a slow charger is provided at the destination but the driver usually stays within 10 minutes and sets off immediately, it is difficult to determine that the driver will fully charge the vehicle and then move. Therefore, the route guidance can be set differently by considering the charging speed information of the destination and the predicted time from arrival at the destination to the next drive.

[0128] Figure 3Case (b) in FIG. 1 shows a case where the driving type is determined to be round-trip driving. Figure 3 As shown in case (b) in , when the driver commutes along a one-way distance of 50 km every day, it can be predicted that when entering the destination at the starting point, the driver will return to the starting point.

[0129] In the case of round trip driving that is expected to return to the starting point, route guidance is provided to guide pre-charging or waypoint charging so that a minimum remaining charge level can be maintained when returning to the starting point. Figure 3 In the diagram of case (b), the driver may be expected to drive 50 km and then return to the starting point, so route guidance is provided so that a charge level that allows the electric vehicle to drive 50 km more than the minimum remaining charge level is maintained upon arrival at the destination.

[0130] In this way, in the case of round-trip driving, when the electric vehicle starts in advance in a state fully charged with electricity or stops at a charging station and fully charged with electricity, the inconvenience of unnecessary visits to the charging station can be prevented and efficient driving can be achieved.

[0131] In the case where it is determined that the driving type is round-trip driving, when there is a charging station where the electric vehicle can stop during driving, a method can be selected between charging on the outbound driving and charging on the return driving. Information related to the weight for each charging factor predicted by the artificial intelligence model can be used to determine the expected charging time, the expected charging location, and the expected charge level, and the weight reflected therein can be used to generate route guidance including a path through the charging station.

[0132] Figures 4 to 7 2 is a diagram illustrating an example of route guidance according to a scene change in a driving information display device according to various exemplary embodiments of the present disclosure.

[0133] In each diagram, the route indicated by each solid line represents the route through which the electric vehicle is guided during the current driving, and the route indicated by each dotted line represents the route through which the electric vehicle is expected to be guided during the next driving after the current driving is completed.

[0134] Figure 4 is a diagram illustrating an example of route guidance that varies depending on the presence / absence of a charging station close to a destination in a driving information display device according to various exemplary embodiments of the present disclosure.

[0135] Even when an electric vehicle drives the same distance at the same state of charge, route guidance may vary depending on whether there are multiple charging stations near the destination in the current driving scenario.

[0136] exist Figure 4In the diagram of , the case of round trip driving intended to return to the starting point is used as an example. Even in the case of one-way driving, information about the status of charging stations near the destination can be utilized.

[0137] like Figure 4 As shown in case (a) in FIG. 1 , when there is a charging station close to the destination, it is easy to return via the charging station on the next drive. Therefore, convenient round-trip driving can be performed in this way: the electric vehicle is guided to drive to the destination without stopping at the charging station, and then passes by the charging station on the way back to the starting point.

[0138] Based on the analysis result of the personalized charging factor artificial intelligence model generated by reflecting the driver's usual habits, it is determined whether to charge the electric vehicle on outgoing driving or on return driving.

[0139] On the contrary, Figure 4 As shown in case (b) in FIG. 1 , when there is no charging station near the destination, a problem occurs when the electric vehicle drives to the charging station on the return drive. Therefore, in this case, the electric vehicle may be guided to stop at the charging station and be charged with electricity in advance, or may be guided to maintain a high charge level at the time of departure, as shown in FIG. Figure 4 As shown in the diagram.

[0140] According to the prior art, the next driving scenario is not considered. Therefore, in both cases, the electric vehicle will be guided to drive via a charging station in advance or drive without a charging station in advance in the same manner. In contrast, in an embodiment of the present disclosure, the charging availability during the next driving is considered, so that the route guidance varies depending on the scenario, and the driver can receive optimized route guidance.

[0141] Various methods according to embodiments of the present disclosure may be applied to implement this. The first method is to input training data as training data to an artificial intelligence model, which derives a weight for each charging factor for the status of a charging station near the destination. Depending on the status of the charging station near the destination, route guidance may be provided with the driver's preferred charging method reflected in the route guidance.

[0142] In another method, when setting the remaining charge level at the destination, as described above, the remaining charge level at the destination may be set to allow the electric vehicle to move more than the minimum remaining charge level to the nearest available charging station after arriving at the destination.

[0143] In this way, in the case where the remaining charge level at the destination is set by considering moving to the nearest charging station, when there is a charging station near the destination, such as Figure 4As shown in case (a) in FIG. 1 , guidance on driving via a charging station may not be provided during the current driving. Figure 4 As shown in case (b) in FIG. 2 , when there is a charging station far from the destination, the electric vehicle may be guided to charge itself to a higher level in advance and then drive to the destination.

[0144] Figure 5 is a diagram illustrating an example of route guidance according to scene changes during round-trip commuting driving in a driving information display device according to various exemplary embodiments of the present disclosure.

[0145] As described above, in the case where it is determined that the driving type is round-trip driving, when a route via a charging station is required to complete the round-trip driving, a method may be selected between charging on the outbound driving and charging on the return driving.

[0146] When guidance is provided so that the minimum remaining charge level is simply maintained at the destination as in the prior art, an inconvenient scenario occurs in which the electric vehicle needs to pass through a charging station on the way out and on the way back. Therefore, using an embodiment of the present disclosure, unlike the prior art, an electric vehicle can be conveniently driven even when any one method is selected. However, using an embodiment of the present disclosure, the driver's usual charging pattern is organized into a database, a personalized charging factor weight artificial intelligence model can be generated by training on the database, the driver's usual preferences for charging location, charging speed, and charging time can be determined, and then guidance on the route is provided based on this information.

[0147] In the case of round-trip commuting driving, such as Figure 5 As shown in case (a) of FIG. 5 , some drivers may prefer to stop by a charging station during peak hours and utilize sufficient power to charge their electric vehicle for the round trip. Figure 5 As shown in case (b) in FIG. 5 , some drivers may prefer to drive directly to work and return to drive by the charging station without stopping.

[0148] This can be selected by determining the weights given to the charging location, charging station, and charging method in combination with the charging factor weights derived from the artificial intelligence model. The personalized charging factor artificial intelligence model is generated by training a database in which the driver's driving and charging scenarios are accumulated. Therefore, the selection is made by reflecting the charging mode according to the driver's driving scenario in the database.

[0149] Therefore, the driver can receive a route recommendation suitable for his or her mode and drive his or her electric vehicle conveniently and efficiently.

[0150] Figure 6is a diagram illustrating an example of route guidance that varies depending on the presence / absence of a slow charger at a destination in a driving information display device according to various exemplary embodiments of the present disclosure.

[0151] Figure 6 The diagram shows a case where the driving type is round trip driving. Figure 6 Case (a) in FIG. 4 shows a case where it is determined that it is desired to stop at a charging station during outbound driving. However, when there is a slow charger at the destination, it is not necessary to increase the remaining charge level at the destination via the charging station.

[0152] Therefore, if Figure 6 As shown in case (b), the remaining charge level of the destination is set to the minimum remaining charge level so that the electric vehicle can immediately reach the destination, and thus the electric vehicle can safely reach the destination.

[0153] When the driver does not stay at his or her destination long enough to fully charge the electric vehicle, a more substantial pre-charge may be required even in the presence of a slow charger.

[0154] Therefore, even when a slow charger is installed at the destination, route guidance may be set differently using information such as the charging speed of the slow charger and the estimated time that the driver is expected to stay at the destination.

[0155] Information about the presence of slow chargers at a destination may be determined based on a database into which charging information is organized when the driver typically charges at the destination. In the case of a location that the driver is visiting for the first time, information about the slow charger may be determined using information about another driver's charging at that location.

[0156] Figure 7 : is a diagram illustrating an example of route guidance that changes depending on the presence / absence of a schedule after arriving at a destination in the driving information display device according to various exemplary embodiments of the present disclosure.

[0157] Figure 7 The diagram of shows an example of route guidance that varies depending on the driver's schedule when the driving type is determined to be round-trip driving in a typical commuting scenario.

[0158] Figure 7 Case (a) in FIG. 1 is a case where another schedule of the driver is not recognized, in which case route guidance can be provided by considering the round-trip driving type and the charging mode of the driver, as shown in the figure.

[0159] In contrast, when the schedule of a driver moving to another location is identified during his or her return drive (driving from work to home), the driving route can be guided by taking into account the movement of the location included in the schedule information, such as Figure 7 In other words, the remaining charge level at the destination is set by considering movement to the next destination after reaching the destination instead of returning to the starting point, so that the minimum remaining charge level can be maintained when arriving at the next destination. In order to meet this requirement, route guidance can be generated so that the driver charges the electric vehicle in advance at the charging station and then drives.

[0160] In this way, even when driving with the same destination and starting point is performed, customized route guidance can be provided by taking various scenarios of the driver into consideration, making efficient and comfortable driving of an electric vehicle feasible.

[0161] Figure 8 is a diagram illustrating a process flow of generating a route guide by classifying a driving type and obtaining a weight for each charging factor in a driving information display device according to various exemplary embodiments of the present disclosure.

[0162] As shown in the figure, using an embodiment of the present disclosure, training data is generated by accumulating various types of data generated from driving scenarios such as starting point / destination information, driving distance, departure day, and departure time, and a model for classifying driving types and a model for obtaining weights for electric vehicle charging factors (electric vehicle characteristics) are generated using the training data.

[0163] Thus, route guidance is generated by making a plan for the route of the electric vehicle. As shown in the figure, first, based on the classification results from the driving type classification model, route candidates including the next driving are derived, so that route guidance considering the scenario of the next route can be realized.

[0164] In the case of a round-trip driving type or a scenario in which the electric vehicle moves to another location after reaching the destination, the remaining charge level at the destination can be determined by taking into account the corresponding driving, thereby supporting the driver to move as comfortably and quickly as possible, not only for the current driving to the destination entered by the driver but also for the overall driving.

[0165] The charging factors for nearby charging stations can be analyzed for various route candidates derived according to the driving type, and the weights derived from the model can be applied to the various candidate analyses. In addition, the remaining charge level at the destination can be changed and set by considering the driving type and various scenarios, and the charging station and the charge level at the charging station can be determined to achieve the remaining charge level at the destination.

[0166] Based on these details, the final electric vehicle driving path is derived and driving guidance is provided to the user.

[0167] Fig. 9 is a flowchart illustrating the flow of a driving information display method according to various exemplary embodiments of the present disclosure.

[0168] In the present exemplary embodiment, the driving information display method is a method performed by the driving information display device 101 and / or the driving information management server 201 including the processor 110 and the storage unit 120. The components described above in conjunction with the operation of the driving information display device 101 and / or the driving information management server 201 can be applied to the driving information display method without significant changes. Therefore, by applying the above description of the driving information display device 101 and the driving information management server 201, those skilled in the art can even implement components that are not specifically described in conjunction with the following driving information display method.

[0169] In the driving type derivation step S901 , the driving type is derived based on driving information received from the electric vehicle including a destination of the electric vehicle.

[0170] In the driving type derivation step S901, the driving type is derived by inputting the driving information of the electric vehicle into a personalized driving type artificial intelligence model, which is generated by performing training using the past driving information of the driver of the electric vehicle as training data.

[0171] In the per-charging factor weight derivation step S902 , the weight for each charging element is derived based on the driving information.

[0172] In the per-charging factor weight derivation step S902, the weight for each charging element is derived by inputting the driving information of the electric vehicle into a personalized charging factor artificial intelligence model, which is generated by performing training using the past driving information of the driver of the electric vehicle as training data.

[0173] In the route information generating step S903 , route information including a path via a charging station is generated using the derived driving type and the weight for each charging factor.

[0174] In the route information generating step S903 , the remaining charge level at the destination is determined using the driving type derived above and the weight for each charging factor, and route information including a path via a charging station is generated to meet the determined remaining charge level at the destination.

[0175] In the route information generating step S903 , the next driving after the current driving may be predicted based on the driving type, and the remaining charge level at the destination may be determined by reflecting information related to the predicted next driving therein.

[0176] In the route information generation step S903, the remaining charge level at the destination is determined so that the minimum remaining charge level is maintained even after additional driving from the destination to the nearest charging station. When the driving type is round-trip driving, the remaining charge level at the destination can be determined so that the minimum remaining charge level can be maintained even after the return driving is completed.

[0177] In the driving information display step S904, driving guidance information including the generated route information may be displayed. In the driving information display step S904, the driving guidance information may be directly displayed on the display screen, or a control signal may be transmitted so that a separate device displays the driving guidance information via the display screen.

[0178] The embodiments of the present disclosure may achieve the advantage of providing driving guide information that enables comfortable driving by taking into account the remaining battery charge level of the electric vehicle.

[0179] Embodiments of the present disclosure may achieve the advantage of improving the overall convenience of driving an electric vehicle by predicting not only driving to a destination but also driving after reaching the destination.

[0180] The embodiments of the present disclosure can achieve the advantage of predicting the driving type as to whether the current driving is round-trip driving or one-way driving by analyzing the driving pattern of each driver and providing route guidance suitable for the driving type.

[0181] Even when the driver drives the same route, embodiments of the present disclosure can achieve the advantage of providing customized route guidance according to circumstances such as the driver's next schedule.

[0182] Furthermore, throughout the present specification, various advantages that can be understood directly or indirectly by those skilled in the art can be provided.

[0183] Although the present disclosure has been described with reference to exemplary embodiments, those skilled in the art may make various modifications and changes to the embodiments of the present disclosure without departing from the spirit and scope of the present disclosure as described in the appended claims.

[0184] The control device may be at least one microprocessor operated by a predetermined program, and the predetermined program may include a series of commands for executing the methods included in the above-described various exemplary embodiments of the present disclosure.

[0185] In various exemplary embodiments of the present disclosure, each of the operations described above may be performed by a control device, and the control device may be configured by a plurality of control devices or an integrated single control device.

[0186] In various exemplary embodiments of the present disclosure, the memory and the processor may be provided as one chip, or provided as separate chips.

[0187] In various exemplary embodiments of the present disclosure, the scope of the present disclosure may include software or machine-executable commands (e.g., operating systems, applications, firmware, programs, etc.) for enabling operations according to the methods of the various embodiments to be executed on a device or computer, and non-transitory computer-readable media including such software or commands stored on the medium and executable on the device or computer.

[0188] In various exemplary embodiments of the present disclosure, the control device may be implemented in the form of hardware or software, or may be implemented in a combination of hardware and software.

[0189] Furthermore, terms such as “unit”, “module”, etc. included in the specification may be a unit for processing at least one function or operation, and the unit may be implemented by hardware, software, or a combination thereof.

[0190] In the exemplary embodiments of the present disclosure, a vehicle may refer to a concept based on various transportation systems including various transportation systems. In some cases, a vehicle may be interpreted as being based on various transportation systems including not only various land transportation vehicles (such as cars, motorcycles, trucks, and buses) driving on roads but also various transportation systems such as airplanes, drones, ships, etc.

[0191] To facilitate interpretation and accurate definition in the appended claims, the terms "upper", "lower", "inner", "outer", "above", "below", "upward", "downward", "front", "back", "rear", "inner", "external", "inwardly", "outwardly", "inner", "outer", "inside", "outside", "forward" and "rearward" are used to describe features of the exemplary embodiments with reference to the positions of such features as displayed in the accompanying drawings. It will also be understood that the term "connected" or its derivatives refer to both direct and indirect connections.

[0192] The term "and / or" may include any combination of a plurality of related listed items or a plurality of related listed items. For example, "A and / or B" includes all three cases, such as "A", "B", and "A and B".

[0193] In this specification, unless otherwise stated, a singular expression includes a plural expression unless the context clearly indicates otherwise.

[0194] In exemplary embodiments of the present disclosure, “at least one of A and B” may refer to “at least one of A or B” or “at least one of a combination of at least one of A and B”. Furthermore, “one or more of A and B” may refer to “one or more of A or B” or “one or more of a combination of one or more of A and B”.

[0195] In the exemplary embodiments of the present disclosure, it should be understood that terms such as “including” or “having” are intended to specify the presence of features, quantities, steps, operations, elements, parts, or combinations thereof described in the specification, and do not exclude the possibility of adding or existing one or more other features, quantities, steps, operations, elements, parts, or combinations thereof.

[0196] The foregoing descriptions of specific exemplary embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise form disclosed, and it is apparent that many modifications and variations are feasible in light of the above teachings. Exemplary embodiments are selected and described to explain certain principles of the present disclosure and their practical applications, so that other persons skilled in the art can make and utilize various exemplary embodiments of the present disclosure and various substitutions and modifications thereof. The scope of the present disclosure is intended to be defined by the appended claims and their equivalents.

Claims

1. A driving information display device, comprising: one or more processors; as well as A storage medium configured to store road information and an algorithm configured to be executed by the one or more processors, and storing computer readable instructions that, when executed by the one or more processors, cause the one or more processors to: performing control to receive driving guidance information and position information of the electric vehicle; and outputting a guidance picture corresponding to the driving guidance information; The driving guidance information includes route information including a path passing through a charging station, and the route information is generated by deriving a driving type based on driving information including a destination of the electric vehicle, deriving a weight for each charging factor based on the driving information, and using the derived driving type and the derived weight for each charging factor.

2. The driving information display device according to claim 1, wherein: The route information is derived by determining a remaining charge level at the destination based on the derived driving type and the derived weight for each charging factor, and includes the path via a charging station to satisfy the determined remaining charge level at the destination.

3. The driving information display device according to claim 2, wherein: The remaining charge level at the destination is determined by predicting a next drive after a current drive based on the driving type, and reflecting information related to the predicted next drive in determining the remaining charge level at the destination.

4. The driving information display device according to claim 3, wherein: The remaining charge level at the destination is determined such that a minimum remaining charge level is maintained even after additional driving from the destination to the nearest charging station.

5. The driving information display device according to claim 3, wherein: The remaining charge level at the destination is determined such that, in response to the driving type being a round-trip driving type, a minimum remaining charge level is maintained even after return driving is completed.

6. The driving information display device according to claim 1, wherein: The driving type is derived by inputting driving information of the electric vehicle into a personalized driving type artificial intelligence model, and the personalized driving type artificial intelligence model is generated by performing training using past driving information of the driver of the electric vehicle as training data.

7. The driving information display device according to claim 1, wherein: The weight for each charging factor is derived by inputting driving information of the electric vehicle into a personalized charging factor artificial intelligence model, which is generated by performing training using past driving information of the driver of the electric vehicle as training data.

8. A driving information management server, comprising: Central processing unit; as well as a memory configured to store instructions which, when executed by the central processing unit, cause the central processing unit to: establishing a connection to exchange information with a plurality of driving information display devices of electric vehicles; deriving a driving type based on driving information including a destination received from a given electric vehicle among the plurality of electric vehicles; deriving a weight for each charging factor based on the driving information; generating route information including a path via a charging station, the route information being generated using the derived driving type and the derived weight for each charging factor; as well as Driving guidance information including the generated route information is transmitted to the electric vehicle.

9. The driving information management server according to claim 8, wherein: The instructions further cause the central processing unit to: determining a remaining charge level at the destination based on the derived driving type and the derived weight for each charging factor; and The route information including a path via a charging station is generated to meet the determined remaining charge level at the destination.

10. The driving information management server according to claim 9, wherein: The instructions further cause the central processing unit to: Based on the driving type, predicting the next driving after the current driving; and In determining the remaining charge level at the destination, the remaining charge level at the destination is determined by reflecting information related to the predicted next driving.

11. The driving information management server according to claim 10, wherein: The instructions further cause the central processing unit to determine the remaining charge level at the destination such that a minimum remaining charge level is maintained even after additional driving from the destination to a nearest charging station.

12. The driving information management server according to claim 10, wherein: The instructions further cause the central processing unit to determine the remaining charge level at the destination so that, in response to the driving type being a round-trip driving type, a minimum remaining charge level is maintained even after return driving is completed.

13. The driving information management server according to claim 8, wherein: The instructions also cause the central processing unit to: deduce the driving type by inputting the driving information of the given electric vehicle into a personalized driving artificial intelligence model, wherein the personalized driving artificial intelligence model is generated by performing training using past driving information of the driver of the given electric vehicle as training data.

14. The driving information management server according to claim 8, wherein: The instructions also cause the central processing unit to derive a weight for each charging factor by inputting the driving information of the given electric vehicle into a personalized charging factor artificial intelligence model, wherein the personalized charging factor artificial intelligence model is generated by performing training using past driving information of the driver of the given electric vehicle as training data.

15. A driving information display method, comprising: deriving a driving type based on driving information received from the electric vehicle including a destination of the electric vehicle; deriving a per-charging factor weight for each charging factor based on the driving information; generating route information including a path via a charging station, the route information being generated using the derived driving type and the derived per-charging factor weight for each charging factor; as well as Driving guidance information including the generated route information is displayed.

16. The driving information display method according to claim 15, wherein: Generating the route information includes: determining a remaining charge level at the destination based on the derived driving type and the derived per-charging factor weight for each charging factor; and The route information including a path via a charging station is generated to meet the determined remaining charge level at the destination.

17. The driving information display method according to claim 16, wherein: Generating the route information includes: predicting a next drive after the current drive based on the driving type; and In determining the remaining charge level at the destination, the remaining charge level at the destination is determined by reflecting information related to a predicted next drive.

18. The driving information display method according to claim 17, wherein: Generating the route information includes determining the remaining charge level at the destination such that a minimum remaining charge level is maintained even after additional driving from the destination to a nearest charging station.

19. The driving information display method according to claim 17, wherein: Generating the route information includes determining the remaining charge level at the destination such that, in response to the driving type being a round-trip driving type, a minimum remaining charge level is maintained even after return driving is completed. 20 . A non-transitory computer-readable storage medium having a program stored thereon, which, when executed by a processor, causes the processor to execute the driving information display method according to claim 15 .

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