Electronic device and method for changing cluster driving vehicle formation

Abstract

The invention relates to an electronic device and a method for changing a cluster driving vehicle formation. The electronic device may include a processor and a memory to store instructions. When executed by the processor, the instructions may enable the electronic device to: identify first information related to a first formation of the vehicle; acquiring second information about whether a second lane distinguished from a first lane where the vehicle is located is at least partially vacant or not during the period of controlling the vehicle in the first formation; determining a second formation based on the second information to move a part of the vehicles to the second lane; based on the second formation, dividing the vehicles into a first group comprising leading vehicles and a second group only comprising following vehicles; and transmitting a signal for moving the second group to the second lane to the subsequent vehicles included in the second group.

Description

Technical Field

[0001] The present disclosure relates to an electronic device and method for changing the formation of clustered vehicles. Background Art

[0002] Swarm driving is a technology that controls autonomous driving of two or more vehicles. Vehicles forming a platoon can travel while forming a certain deformation. By reducing the distance between vehicles, swarm driving can reduce air resistance, thereby improving fuel efficiency and reducing the risk of accidents, while also regulating traffic flow and reducing traffic congestion. The vehicles forming a platoon may include a leading vehicle and following vehicles. Electronic devices configured in the leading vehicle can be used to control swarm driving. For example, the electronic devices can identify the surrounding environment, set a driving route based on the surrounding environment, and control the speed and direction of the vehicle. For example, the electronic devices can determine the formation of the swarm. Summary of the Invention

[0003] Depending on the formation of the swarm, the swarm may separate, and the swarm formation may need to be changed as needed.

[0004] An electronic device for platooning vehicles is provided. According to one embodiment, the electronic device may include a processor and a memory for storing instructions. When the instructions are executed by the processor, the electronic device may be capable of: identifying first information related to a first formation of the vehicles; obtaining second information related to whether a second lane, which is distinct from the first lane in which the vehicles are located, is at least partially vacant while the vehicles are controlled in the first formation; determining a second formation based on the second information so as to move a portion of the vehicles to the second lane; dividing the vehicles into a first group including a leading vehicle and a second group including only following vehicles based on the second formation; and sending a signal to the following vehicles included in the second group for moving the second group to the second lane.

[0005] A method performed by an electronic device is provided. According to one embodiment, the method of the electronic device may include: identifying first information related to a first formation of clustered vehicles. The method may include: obtaining second information related to whether a second lane, which is distinguished from the first lane in which the vehicle is located, is at least partially vacant during the period when the vehicle is controlled in the first formation. The method may include: determining a second formation based on the second information so that a portion of the vehicles moves to the second lane. The method may include: distinguishing the vehicles into a first group including a leading vehicle and a second group including only following vehicles based on the second formation. The method may include: sending a signal to the following vehicles included in the second group for moving the second group to the second lane.

[0006] When the vehicles are controlled in cluster driving mode, the electronic device can timely change the cluster formation according to road conditions. By changing the cluster formation, the separation of the cluster can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 Platooning vehicles are schematically shown.

[0008] Figure 2 FIG. 1 is a block diagram of an electronic device for cluster driving of vehicles according to an embodiment.

[0009] Figure 3 FIG. 1 is a flow chart illustrating the operation of an electronic device for changing a cluster formation.

[0010] Figure 4 The schematic shows the Figure 3 The operation is a process of changing the cluster formation.

[0011] Figure 5 is a flow chart illustrating the operation of an electronic device for changing the formation of a convoy in the presence of other vehicles.

[0012] Figure 6 The schematic shows the Figure 5 The operation is a process of changing the cluster formation.

[0013] Figure 7 1 is a flowchart illustrating the operation of the electronic device for changing the cluster formation when the second lane line includes a plurality of lane lines.

[0014] Figure 8 The schematic shows the Figure 7The operation is a process of changing the cluster formation.

[0015] Figure 9 The process of changing the cluster formation is schematically shown.

[0016] Figure 10a 、 Figure 10b and Figure 10c An example of a process in which an electronic device generates a local map based on environmental information received from a vehicle according to an embodiment is shown.

[0017] Figure 11 Another example of a process in which an electronic device generates a local map based on environmental information received from a vehicle according to an embodiment is shown.

[0018] Figure 12 An example block diagram of an autonomous driving system for a vehicle according to an embodiment is shown.

[0019] Figure 13 and Figure 14 An example block diagram of an autonomous driving mobile body according to an embodiment is shown.

[0020] Figure 15 An example of a gateway in relation to a user device according to various embodiments is shown.

[0021] Figure 16 is a diagram for describing the operation of an electronic device for training a neural network based on a training data set according to one embodiment.

[0022] Figure 17 is a block diagram of an electronic device according to an embodiment.

[0023] Figure 18a and Figure 18b An example of vehicles performing cluster driving is shown. DETAILED DESCRIPTION

[0024] The electronic devices involved in the various embodiments disclosed herein may be devices of various forms. For example, the electronic devices may include portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, electronic devices, or household appliances. The electronic devices according to the embodiments of this document are not limited to the above-mentioned devices.

[0025] The various embodiments in this document and the terms used therein should not limit the technical features in this document to specific embodiments, but should be understood to include various modifications, equivalents or substitutes of these embodiments. With respect to the description of the drawings, similar figure marks may be used for similar or related constituent elements. Unless otherwise clearly indicated by the relevant context, the singular form of the noun corresponding to the item may include one or more of the above-mentioned items. In this document, each of phrases such as "A or B", "at least one of A and B", "at least one of A or B", "A, B or C", "at least one of A, B and C", and "at least one of A, B or C" may include items related to any of the items listed in these phrases, or all possible combinations thereof. Terms such as "first", "second" or "first", "second" are only used to distinguish a constituent element from other constituent elements, and do not limit these constituent elements in other aspects (such as importance or order). If a certain (for example, a first) component is “functionally” or “communicatively” coupled or connected with another (for example, a second) component, or in the absence of these terms, it means that any of the above components can be connected to the other component directly (for example, by wire), wirelessly, or through a third component.

[0026] The term "module" used in various embodiments herein may include units implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be a component constructed as a whole, or the smallest unit of the component or a portion thereof that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an ASIC (application-specific integrated circuit).

[0027] The various embodiments herein may be implemented by software (e.g., a program) comprising one or more instructions, which is stored in a storage medium (e.g., a built-in memory or an external memory) that can be read by a machine (e.g., an electronic device 100). For example, a processor (e.g., processor 110) of a device (e.g., an electronic device 100) may call and execute at least one of the one or more instructions stored in the storage medium. This enables the device to perform at least one function according to the at least one instruction called. The one or more instructions may include code generated by a compiler, or code that can be executed by an interpreter. The storage medium that can be read by the device may be provided in the form of a non-transitory storage medium. Here, "non-transitory" only means that the storage medium is a tangible device and does not include a signal (e.g., an electromagnetic wave), and does not distinguish whether the data is semi-permanently stored or temporarily stored in the storage medium.

[0028] According to one embodiment, the methods involved in the various embodiments disclosed herein may be included in a computer program product. The computer program product may be traded as a commodity between a seller and a buyer. The computer program product may be distributed in the form of a device-readable storage medium (e.g., a compact disc read only memory (CD-ROM)) or distributed through an application store (e.g., PlayStore). TM ), or distributed (e.g., downloaded or uploaded) directly or online between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored in a device-readable storage medium, such as a memory of a manufacturer's server, an app store server, or a relay server, or may be temporarily generated.

[0029] According to various embodiments, each constituent element (e.g., module or program) in the above-mentioned constituent elements may include a single or multiple objects, wherein parts of the multiple objects may be configured separately from other constituent elements. According to various embodiments, one or more constituent elements or operations in the above-mentioned constituent elements may be omitted, or one or more other constituent elements or operations may be added. Alternatively or in addition, multiple constituent elements (e.g., module or program) may be integrated into one constituent element. In this case, for one or more functions of each constituent element in the multiple constituent elements, the integrated constituent element may be executed in a manner identical or similar to the function performed by the corresponding constituent element before integration. According to various embodiments, the operations performed by modules, programs or other constituent elements may be performed sequentially, in parallel, repeatedly or in a heuristic manner, and one or more of the above-mentioned operations may be performed in a different order, omitted, or one or more other operations may be added.

[0030] Hereinafter, embodiments of this invention will be described with reference to the accompanying drawings.

[0031] Figure 1 Platooning vehicles are schematically shown.

[0032] Cluster driving is a technology that controls two or more vehicles 10 forming a platoon to maintain a specified formation. Each of the vehicles 10 may include an electronic device for cluster driving (e.g., Figure 2 The electronic devices 100 and 200 can use V2X (Vehicle To Everything) communication technology to share control information of the vehicle 10 and information collected by the electronic devices 100 and 200 arranged on the vehicle 10 in real time. Figure 1 The wireless communication technology used to exchange information between the electronic devices 100 and 200 shown can use various wireless access technologies (Wireless Access Technologies) such as V2X (Vehicle to Everything) including V2I (Vehicle to Infrastructure), V2D (Vehicle to Device), V2V (Vehicle to Vehicle), V2P (Vehicle to Pedestrian), 5G NR (New Radio) Sidelink (cellular), and 802.11-based short-range dedicated communication (Dedicated Short Range Communication: DSRC).

[0033] Vehicles 10 can be divided into a leading vehicle 11 and following vehicles 12. Leading vehicle 11 can be defined as the vehicle located at the front of a group of vehicles 10, while following vehicles 12 are vehicles other than leading vehicle 11. An electronic device 100 located in leading vehicle 11 can be used to control the overall operation of the group. For example, because leading vehicle 11 is located at the front of the group, electronic device 100 can obtain more information than other electronic devices 200.

[0034] The electronic device 100 can transmit and / or receive data with an external electronic device (e.g., base station 13 and / or satellite 14). For example, to determine a driving route, the electronic device 100 can receive data containing information related to the driving route from the external electronic devices 13 and 14, and can send data containing information related to the real-time location of the cluster to the external electronic devices 13 and 14.

[0035] The electronic device 100 can control the driving of the vehicles 10 based on information related to the swarm-traveling vehicles 10 (e.g., the driving route, driving speed, the distance between vehicles 10, and / or the formation of the swarm) and / or information related to the surrounding environment (e.g., road conditions, other vehicles 20, lane lines 30, and / or lanes 40). For example, the electronic device 100 can send a signal for controlling the swarm driving to another electronic device 200 disposed in each subsequent vehicle 12. The other electronic device 200 can then control the driving of the subsequent vehicle 12 based on the signal received from the electronic device 100.

[0036] While the vehicles 10 are being controlled in cluster driving mode, cluster separation may occur. For example, other vehicles 20 may be inserted between the vehicles 10, or when the vehicles 10 pass through the traffic light 50, the cluster may separate because not all vehicles 10 in the cluster have passed through the traffic light 50. When the vehicles 10 separate into multiple groups, the traffic lights 50 located on the driving route may increase the distance between the multiple groups. For example, when the cluster separates into a leading group and a trailing group, the leading group may pass directly without stopping at the traffic light 50, while the trailing group waits at the traffic light 50 before passing, thereby increasing the distance between the leading group and the trailing group. Since it is difficult to control the cluster driving of the vehicles 10 when the cluster separates, in order to re-merge the separated groups, problems such as rescheduling the driving route, rerouting the driving route, or re-merging the leading group into a cluster after waiting at a specific location may occur.

[0037] According to one embodiment, the electronic device 100 for cluster driving recognizes the presence of other lanes (e.g., the second lane 42) that are different from the driving lane of the vehicle 10 (e.g., the first lane 41) while the vehicle 10 is controlled in the cluster driving mode, and can change the formation of the cluster when the other lanes are at least partially empty. For example, the formation of the cluster may change while waiting for a signal from a traffic light 50. By changing the formation of the cluster, the separation of the cluster can be reduced. For example, while the vehicle 10 is waiting for a signal, a portion of the vehicle 10 can be moved to another idle lane to change the formation, and a quick departure can be achieved when the signal changes, thereby reducing the separation of the cluster.

[0038] The following describes an electronic device 100 for tethered driving that can change tethered formations, with reference to the accompanying drawings. In this disclosure, terms such as "first lane" and "second lane" are used solely to distinguish lanes. For example, the term "first lane" refers to the lane in which vehicles 10 maintain the first formation before a change. It does not, for example, refer to a lane near a center line as defined by law or a lane where lane changes are prohibited.

[0039] Figure 2 FIG. 1 is a block diagram of an electronic device for cluster driving of vehicles according to an embodiment.

[0040] Reference Figure 2 The electronic device 100 according to an embodiment may include a processor 110, a memory 120, a wireless communication device 130, a camera 140 and / or a GPS (Global Positioning System) sensor 150. The electronic device 100 according to an embodiment may be referred to as being arranged on a leading vehicle (e.g., Figure 1 An electronic device within a vehicle 11).

[0041] For example, the processor 110, the memory 120, the wireless communication device 130, the camera 140, and / or the GPS sensor 150 may be electrically and / or operatively coupled to each other via electronic components such as a communication bus. Hereinafter, the operative coupling of hardware may refer to a direct or indirect connection between hardware components established by wire or wirelessly, such that a first hardware component in the hardware can control a second hardware component.

[0042] exist Figure 2 In the embodiment, the processor 110, the memory 120, the camera 140, the wireless communication device 130 and / or the GPS sensor 150 are shown as different modules, but are not limited thereto. Figure 2 Portions of the hardware shown may be implemented as part of a single integrated circuit (IC) or a single package, such as a SoC (system on a chip).

[0043] According to one embodiment, the memory 120 may store instructions. The processor 110 may be configured to process data based on the instructions stored in the memory 120. For example, the processor 110 may include an arithmetic and logic unit (ALU), a floating point unit (FPU), a field programmable gate array (FPGA), a central processing unit (CPU), and / or an application processor (AP). The processor 110 may have a single-core processor structure or a multi-core processor structure, such as a dual-core, quad-core, hexa-core, or octa-core processor.

[0044] According to one embodiment, the memory 120 may include hardware components for storing data and / or instructions, which may be executed by the processor 110. For example, the memory 120 may include volatile memory, such as random-access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM). For example, the volatile memory may include at least one of dynamic random access memory (DRAM), static random access memory (SRAM), cache RAM, and pseudo-static random access memory (PSRAM). For example, the non-volatile memory may include at least one of a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a hard disk, an optical disk, a solid state drive (SSD), and an embedded multi-media card (eMMC). For example, the memory 120 of the electronic device 100 may include a neural network model. The electronic device 100 may recognize external objects (e.g., lane lines (e.g., Figure 1 Lane lines 30 in the lane (e.g., Figure 1 40 in the lane), other vehicles (e.g. Figure 1 other vehicles 20) and / or signal lights (e.g., Figure 1 signal light 50).

[0045] According to one embodiment, the wireless communication device 130 may be used to communicate wirelessly with other electronic devices 200 and / or external electronic devices. For example, the electronic device 100 may communicate with an external electronic device (e.g., a base station (e.g., Figure 1 Base stations 13 in) and / or satellites (e.g., Figure 1 The wireless communication device 130 may be electrically connected to an antenna (e.g., Figure 14The wireless communication device 130 can convert the analog signal from the processor 110 into a digital signal and upconvert the baseband signal into a radio frequency (RF) signal. The electronic device 100 can use the GPS sensor 150 to obtain information related to the real-time location of the cluster and send data containing the information to the external electronic devices 13 and 14 through the wireless communication device 130. The electronic device 100 can send data for controlling subsequent vehicles (for example, Figure 1 The other electronic devices 200 may receive the signal via the wireless communication device 230.

[0046] According to one embodiment, the camera 140 may include a lens assembly or an image sensor. The lens assembly is capable of collecting light emitted from a subject that is the subject of image capture. The lens assembly may include one or more lenses. For example, the camera 140 may include multiple lens assemblies. For example, among the multiple lens assemblies of the camera 140, some lens assemblies may have the same lens properties (e.g., viewing angle, focal length, autofocus, f-number, or optical zoom), while at least one lens assembly may have one or more lens properties different from those of the other lens assemblies. The lens assembly may include a wide-angle lens or a telephoto lens. For example, the electronic device 100 may include a flash for the camera 140. The flash may include one or more light-emitting diodes (e.g., RGB (red-green-blue) LEDs, white LEDs, infrared LEDs, or ultraviolet LEDs), or a xenon lamp. For example, the image sensor may convert light emitted from or reflected from the subject and transmitted through the lens assembly into an electrical signal, thereby acquiring an image corresponding to the subject. According to one embodiment, the image sensor may include one image sensor selected from sensors having different properties, such as an RGB sensor, a black and white (BW) sensor, an infrared (IR) sensor, or an ultraviolet (UV) sensor; multiple image sensors having the same properties; or multiple image sensors having different properties. Each image sensor included in the image sensor may be implemented, for example, using a CCD (charged coupled device) sensor or a CMOS (complementary metal oxide semiconductor) sensor.

[0047] According to one embodiment, the electronic device 100 can recognize the environment around the leading vehicle 11 through the camera 140. For example, the electronic device 100 can recognize external objects based on the image acquired through the camera 140. For example, the electronic device 100 can use the above-mentioned neural network model to recognize external objects corresponding to the image acquired through the camera 140. For example, the electronic device 100 can use the camera 140 to acquire images of vehicles in other lanes (e.g., Figure 1 An image corresponding to another vehicle 20 traveling in the second lane 42 in the image is obtained, and the other vehicle 20 in the other lane 42 is identified from the image.

[0048] The other electronic devices 200 disposed in the following vehicle 12 may include substantially the same components as the electronic device 100 disposed in the leading vehicle 11. For example, each other electronic device 200 may include a processor 210, a memory 220, a wireless communication device 230, a camera 240, and / or a GPS sensor 250. The description of the components of the electronic device 100 may be substantially the same as the components of the other electronic devices 200.

[0049] Because the vehicles 10 maintain a designated formation, the cameras 240 of other electronic devices 200 may capture images that are not available to the camera 140 of the electronic device 100 at certain specific times. According to one embodiment, the other electronic devices 200 may send information related to the images captured by the cameras 240 and / or information related to external objects identified from the images to the electronic device 100. Based on the information received from the other electronic devices 200, the electronic device 100 can identify the surrounding environment of the group and control the movement of the group based on the surrounding environment.

[0050] According to one embodiment, the electronic device 100 can use a neural network model to change the formation. For example, the processor 110 can determine whether to change the formation based on the information about the surrounding environment of the leading vehicle 11 obtained by the camera 140 (e.g., first environmental information) and the information received from the following vehicle 12 (e.g., second environmental information). For example, when the vehicle is in the same lane as the cluster (e.g., Figure 1 The first lane 41) is different from the other lanes (e.g., Figure 1When the second lane 42 of the following vehicle 12 is at least partially vacant, the processor 110 may change the formation to move a portion of the following vehicle 12 to the other lane 42. The electronic device 100 may send a signal to the other vehicle 20 to move a portion of the following vehicle 12 to the other lane 42. Upon receiving the signal, the portion of the following vehicle 12 may move to the at least partially vacant other lane 42. As the portion of the following vehicle 12 moves to the other lane, the formation is changed, and due to the change in formation, cluster separation may be reduced.

[0051] Figure 3 FIG. 1 is a flow chart illustrating the operation of an electronic device for changing a cluster formation. Figure 4 The schematic shows the Figure 3 The operation is a process of changing the cluster formation.

[0052] Figure 3 The operations described in the above may be performed by a computer stored in a memory (e.g., Figure 2 The instructions in the memory 120) are sent by the processor (e.g., Figure 1 When executed by the processor 110 of the electronic device (for example, Figure 2 In the following description, the electronic device 100 deployed in the lead vehicle (e.g., Figure 1 The electronic device 100 in the leading vehicle 11 of FIG. 1 is referred to as the first electronic device 310 , while the electronic device 100 deployed in the following vehicle (eg, Figure 1 Other electronic devices (e.g., Figure 2 The other electronic device 200 is referred to as (refer to) the second electronic device 320.

[0053] Reference Figure 3 In operation 301, the first electronic device 310 may identify the vehicles 10 (eg, Figure 4 First information related to the first formation of vehicles 10).

[0054] For example, when executing instructions stored in memory 120, processor 110 may obtain first information related to a first formation of vehicles 10. The first formation may be referred to as the formation of the cluster before the change. The formation of the cluster may be defined as columns and rows. A column refers to a line of vehicles 10 arranged in the direction of travel of the vehicles 10 forming the cluster (e.g., a vertical line). A row refers to a line of vehicles 10 arranged in a direction perpendicular to the direction of travel of the vehicles 10 forming the cluster (e.g., a horizontal line).

[0055] Figure 4 400a represents the vehicles 10 traveling in a cluster in the formation before the change, that is, the first formation. Figure 4 400a, when the vehicle 10 includes a leading vehicle 11 and five following vehicles 12 (e.g., a first vehicle 12-1, a second vehicle 12-2, a third vehicle 12-3, a fourth vehicle 12-4 and / or a fifth vehicle 12-5), the first formation may be composed of 6 rows and 1 column (e.g., 6×1). The first electronic device 310 may send a signal for vehicle driving control to each second electronic device 320. Based on the above signal, the following vehicles 12 may follow the leading vehicle 11 while maintaining a specified interval between the vehicles 10. The first electronic device 310 may identify first information related to the first formation in real time while the vehicle 10 is controlled in cluster driving mode. For example, in operation 301, the first electronic device 310 may identify first information related to the first formation consisting of 6 rows and 1 column.

[0056] In operation 302 , the first electronic device 310 may acquire first environmental information related to the surrounding environment of the lead vehicle 11 .

[0057] For example, when the processor 110 executes the instructions stored in the memory 120, it can be based on the data from the camera (for example, Figure 2 The first electronic device 310 can use the camera 140 to obtain images to obtain first environmental information related to the surrounding environment of the leading vehicle 11. The first electronic device 310 can use the camera 140 to obtain images. The camera 140 can provide the processor 110 with images of the surrounding environment of the leading vehicle 11. The processor 110 can obtain first information related to the surrounding environment of the leading vehicle 11 by identifying external objects in the acquired images. The first electronic device 310 can use a neural network to identify external objects in the acquired images. For example, the first electronic device 310 can use a neural network model pre-trained for identifying external objects to identify external objects such as other vehicles 20, lane lines 30, lanes 40 and / or signal lights 50 around the leading vehicle 11 from images corresponding to the external objects.

[0058] In operation 303, the second electronic device 320 may obtain second environmental information related to the surrounding environment of the following vehicle 12. In operation 303, the term "following vehicle" is a relative term used to distinguish it from the "leading vehicle" and may be used to refer to the vehicle equipped with the second electronic device 320.

[0059] For example, a processor (e.g., Figure 2 The processor 210) executes the data stored in the memory (eg, Figure 2 220) can be based on the instructions from the camera (for example, Figure 2 The image obtained by the camera 240 is used to obtain the second environmental information related to the surrounding environment of each subsequent vehicle 12.

[0060] Reference Figure 4 Because the first angle of view 411 of the camera 140 deployed in the leading vehicle 11 may be limited, there may be areas (e.g., blind spots) where the lens assembly of the camera 140 cannot collect light emitted by the subject. For example, external objects located within the blind spot of the camera 140 cannot be captured by the camera 140, and therefore, at least some of the external objects located around the vehicle 10 may not be included in the first environment information.

[0061] According to one embodiment, since the following vehicle 12 follows the leading vehicle 11, the camera 240 can collect light emitted from external objects located in the blind spot. The camera 240 can provide the processor 210 with images of the surrounding environment of each following vehicle 12. The processor 210 can obtain second environmental information related to the surrounding environment of the following vehicle 12 by identifying external objects in the acquired image. As mentioned above, the second electronic device 320 can also use a neural network to identify external objects in the acquired image. Since the first perspective 411 of the camera 140 partially overlaps with the second perspective 412 of the camera 240, the first environmental information and the second environmental information may contain duplicate information.

[0062] In operation 304 , the first electronic device 310 may receive second environmental information related to the surrounding environment of the following vehicle 12 from the following vehicle 12 .

[0063] For example, when executing instructions stored in the memory 120, the processor 110 may receive data including the second environment information sent from the wireless communication device 230 via the wireless communication device 130. The second environment information may be received in real time.

[0064] In operation 305 , while controlling the vehicles 10 to travel in the first formation, the first electronic device 310 may obtain second information related to whether at least a portion of a second lane 42 different from the first lane 41 where the vehicles 10 are located is vacant.

[0065] For example, when executing the instructions stored in the memory 120, the processor 110 may obtain second information related to whether at least a portion of the second lane 42 is vacant based on the first environment information and the second environment information. The first lane 41 may be referred to as the lane where the vehicles 10 traveling in a cluster in the first formation are located. The second lane 42 may be referred to as a lane different from the first lane 41, that is, a lane where the vehicles 10 traveling in a cluster are not located. For example, Figure 4 As shown, the first formation consists of 6 rows and 1 column and when traveling in the left lane, the first lane 41 can be the left lane and the second lane 42 can be the right lane.

[0066] According to one embodiment, the first electronic device 310 may obtain second information related to whether at least a portion of the second lane 42 is vacant based on the first environmental information and the second environmental information. Figure 4 As shown, when there are no other vehicles in the second lane 42 (e.g., Figure 1 When there is no image corresponding to the other vehicle 20 in the images obtained from the camera 140 and the camera 240, the first electronic device 310 can obtain the second information indicating that the second lane 42 is vacant based on the first environmental information and the second environmental information.

[0067] In operation 306 , the first electronic device 310 may determine a second formation based on the second information to move some of the vehicles 10 to the second lane 42 .

[0068] For example, when executing instructions stored in the memory 120 , the processor 110 may determine a second formation to be changed from the first formation based on the second information to prevent the cluster from separating.

[0069] Figure 4 400b shows the vehicles 10 traveling in a changed formation, i.e., a second formation. For example, the second formation may include row information and column information corresponding to each vehicle in the second formation, so that the vehicles travel in a cluster according to the second formation. For example, each vehicle forming the second formation may be assigned a designated position based on the row information and column information. According to one embodiment, the processor 110 may determine the second formation based on the number of lanes in order to efficiently change the cluster. Figure 4 In 400b, the second formation may be composed of 3 rows and 2 columns (e.g., 3×2). Since the second lane 42 only contains one lane and is completely empty, when the vehicles 10 are divided into groups of 3 and arranged in 2 columns, the vehicles 10 can stably maintain cluster driving. For example, when the second lane 42, which is different from the first lane 41, only contains one lane and there are no other vehicles in the second lane 42 (e.g., Figure 6 When the other vehicles 20 are present, the processor 110 may determine a second formation including 2 columns.

[0070] According to one embodiment, the rows of the second formation (e.g., 3 rows) may be smaller than the rows of the first formation (e.g., 6 rows); and the columns of the second formation (e.g., 2 columns) may be larger than the columns of the first formation (e.g., 1 column). In this case, because the second formation is shorter than the first formation, cluster separation can be prevented. However, the above description applies to formation changes that prevent cluster separation and does not necessarily require that the formation must have fewer columns after the change. For example, the cluster formation can be appropriately changed based on the number of lanes, traffic conditions, etc.

[0071] In operation 307 , the first electronic device 310 may divide the vehicles 10 into a first group including the leading vehicle 11 and a second group including only the following vehicles 12 based on the second formation.

[0072] For example, when executing instructions stored in the memory 120, the processor 110 may divide the vehicles 10 into a plurality of groups based on the second formation in order to change the formation. The plurality of groups may be determined based on the columns of the second formation.

[0073] For example, if the vehicles 10 forming the cluster include 6 vehicles and the second formation includes 2 columns, the processor 110 may divide the vehicles 10 into groups of 3 vehicles each and assign them to the first group and the second group respectively. Figure 4 In 400 b , when the second formation includes two columns, the processor 110 may divide the vehicles 10 into a first group and a second group corresponding to the two columns, respectively.

[0074] For example, the first group may include the leading vehicle 11. The first group may include only the leading vehicle 11, or may include the leading vehicle 11 and a portion of the following vehicles 12. Figure 4 In the example shown in , the first group may include the lead vehicle 11 , a first vehicle 12 - 1 , and a second vehicle 12 - 2 .

[0075] For example, the second group may include only the following vehicle 12. Since the leading vehicle 11 is included in the first group, the second group may be a vehicle group consisting of only the following vehicle 12. Figure 4 In the example shown, the second group may include the third vehicle 12-3, the fourth vehicle 12-4, and the fifth vehicle 12-5. The processor 110 may distinguish the vehicles 10 based on the second formation into a first group including the lead vehicle 11, the first vehicle 12-1, and the second vehicle 12-2, and a second group including the third vehicle 12-3, the fourth vehicle 12-4, and the fifth vehicle 12-5.

[0076] In operation 308 , the first electronic device 310 may transmit a signal to the following vehicles 12 included in the second group to move the second group to the second lane 42 .

[0077] For example, when the processor 110 executes the instructions stored in the memory 120, in order to make the subsequent vehicles (e.g., the third vehicle 12-3, the fourth vehicle 12-4, and the fifth vehicle 12-5) belonging to the second group move to the second lane 42, the wireless communication device 130 may be used to send a signal for driving control of the subsequent vehicles 12 to the subsequent vehicles 12-3, 12-4, and 12-5. The signal for driving control may be provided by the first electronic device 310. Figure 4The first electronic device 310 can send the signal so that the subsequent vehicles 12 belonging to the first group (for example, the first vehicle 12-1 and the second vehicle 12-2) do not change lanes, and the subsequent vehicles 12-3, 12-4, and 12-5 belonging to the second group drive to the second lane 42.

[0078] According to one embodiment, the data packet of the signal may include a target identifier representing the vehicle 10 that will receive the signal. For example, a unique identifier may be assigned to each subsequent vehicle 12. The target identifier may include identifiers assigned to the subsequent vehicles 12-3, 12-4, and 12-5 in the second group. Since the target identifier is included in the data packet of the signal, even if the subsequent vehicles 12-2 and 12-3 belonging to the first group receive the signal, they can maintain driving in the first lane 41 by the target identifier included in the data packet. However, this is not limited to this. As an alternative, the first electronic device 310 may use the signal characteristics of the signal, such as frequency, amplitude, and phase, to send a signal so that only the subsequent vehicles 12-3, 12-4, and 12-5 in the second group move.

[0079] According to one embodiment, the following vehicles 12-3, 12-4, 12-5 in the second group may move from the first lane 41 to the second lane 42 based on the reception of the signal. Figure 4 As shown in FIG. 400 b , the formation of the cluster may be changed from the first formation to the second formation by the following vehicles 12 - 3 , 12 - 4 , and 12 - 5 moving to the second lane 42 .

[0080] According to one embodiment, the formation change may be performed while the vehicle 10 is waiting for a signal from the traffic light 50. For example, while the vehicle 10 is stopped by a stop signal (e.g., a red signal) from the traffic light 50, the processor 110 may send a signal to the following vehicles 12-3, 12-4, and 12-5 belonging to the second group to move the second group to the second lane 42. While the vehicle 10 is waiting for the stop signal from the traffic light 50, the following vehicles 12-3, 12-4, and 12-5 belonging to the second group may move to the second lane 42.

[0081] According to one embodiment, when the signal waiting time of the traffic light 50 is greater than the time required for the subsequent vehicles 12-3, 12-4, and 12-5 included in the second group to move, the first electronic device 310 can send a signal to move the second group to the second lane 42. For example, the processor 110 can obtain first time information related to the remaining time of the stop time of the traffic light 50 from an external electronic device (e.g., a base station and / or a satellite), the traffic light 50, and / or a navigation application.

[0082] According to one embodiment, the processor 110 may calculate second time information related to the time required to change from the first formation to the second formation. For example, the second time information may be calculated based on the time required for the following vehicles 12-3, 12-4, and 12-5 belonging to the second group to move from the first lane 41 to the second lane 42. For example, the processor 110 may calculate the second time information based on the number, speed, and / or travel distance of the following vehicles 12-3, 12-4, and 12-5 belonging to the second group.

[0083] According to one embodiment, processor 110 may be configured to compare the first time information with the second time information and, based on the second time information being shorter than the first time information, send a signal to move the second group to second lane 42. For example, if the second time information is longer than the first time information, then if the stop signal changes to a go signal (e.g., a green signal) while the subsequent vehicles 12-3, 12-4, and 12-5 in the second group are moving, traffic congestion or a traffic accident may occur due to a lane change. If the second time information is shorter than the first time information, the subsequent vehicles 12-3, 12-4, and 12-5 in the second group may move from first lane 41 to second lane 42 before the stop signal changes to a go signal, thereby preventing traffic congestion or a traffic accident. According to one embodiment, processor 110 may move the second group to second lane 42 based on the remaining time of the stop signal on traffic light 50 and the time required for the lane change.

[0084] Figure 3 and Figure 4 The example described above assumes that no other vehicle 20 exists in the second lane 42, but the operation for changing the formation can also be performed when there is another vehicle 20 in the second lane 42. The following describes an example operation for changing the formation when there is another vehicle 20 in the second lane 42.

[0085] Figure 5 is a flowchart illustrating the operation of an electronic device for changing the formation of a convoy in the presence of other vehicles. Figure 6 The schematic shows the Figure 5 The operation is a process of changing the cluster formation.

[0086] Figure 5 The operations described in the may be in memory (e.g., Figure 2 The instructions stored in the memory 120 of the processor (e.g., Figure 2 When executed by the processor 110 of the electronic device (for example, Figure 2 Operations performed by the electronic device 100).

[0087] refer to Figure 5 In operation 501, the electronic device 100 can identify the second lane (eg, Figure 6 The first area of ​​the second lane 42) (eg, Figure 6 ) and the second area (e.g., Figure 6 second region 612).

[0088] For example, when executing instructions stored in the memory 120, the processor 110 can identify, based on the second information, the first area 611 of the second lane 42 that is at least partially vacant and the area occupied by other vehicles in the second lane 42 (e.g., Figure 6 For example, the processor 110 may determine whether the first area 611 on the second lane 42 is occupied by other vehicles or the like by performing object detection on the image acquired by the camera 140.

[0089] Figure 6 600a represents the vehicles 10 traveling in a cluster in a formation before the change, that is, a first formation. For example, the first formation may be composed of 6 rows and 1 column. Figure 6 , the other vehicles 20 may be located in the second lane 42. For example, the other vehicles 20 may be located next to the lead vehicle 11. When the other vehicles 20 are located in the second lane 42, the second formation may be determined based on the area of ​​the second lane 42 occupied by the other vehicles 20.

[0090] According to one embodiment, the processor 110 may divide the second lane 42 into a first area 611 and a second area 612. The first area 611, as a vacant portion of the second lane 42, may be an area of ​​the second lane 42 that is not occupied by other vehicles 20. For example, when there is no other vehicle 20 next to the following vehicle 12, the processor 110 may identify the vacant first area 611 based on the second environmental information.

[0091] For example, the second area 612, as another portion of the second lane 42 occupied by another vehicle 20, may be an area of ​​the second lane 42 that is not vacant. According to one embodiment, the processor 110 may identify the other vehicle 20 located next to the lead vehicle 11 using the first environmental information. For example, the camera 140 may capture an image containing the other vehicle 20 located next to the lead vehicle 11. The processor 110 may recognize the presence of the other vehicle 20 from the image. However, this is not limiting. For example, when the other vehicle 20 is located within the first viewing angle 411 of the first camera 140 and the second viewing angle 412 of the camera 240, the other vehicle 20 may also appear in the image captured by the camera 240. In this case, the processor 110 may recognize the presence of the other vehicle 20 based on the first and second environmental information. The processor 110 may identify the first and second areas 611, 612 based on the first and / or second environmental information. Based on the identification of the first and second areas 611, 612, the processor 110 may obtain second information indicating that a portion of the second lane 42 is occupied by the other vehicle 20.

[0092] In operation 503 , the electronic device 100 may determine a second formation based on the first area 611 and the second area 612 .

[0093] For example, when executing the instructions stored in the memory 120, the processor 110 may determine the second formation based on the first area 611 and the second area 612 to move the second group to the first area 611 of the second lane 42. When there are other vehicles 20 in the second lane 42, if the cluster is changed to Figure 4 If the second formation is formed as shown in FIG400 b, the subsequent vehicle (eg, the third vehicle) in the second group may collide with the other vehicle 20. The processor 110 may determine the second formation based on the first area 611 and the second area 612 to prevent the above-mentioned collision.

[0094] Figure 6 600b represents the vehicles 10 traveling in a cluster in a changed formation, ie, a second formation. Figure 6, the processor 110 may determine a second formation so that the second group moves to the first area 611 not occupied by other vehicles 20. For example, the second formation may be determined so that the following vehicles 12 included in the second group (e.g., the third, fourth, and fifth vehicles) are located behind the other vehicles 20 in the second lane 42. For example, the second formation may consist of 4 rows and 2 columns (e.g., 4×2), but the vehicles 10 may not be located next to the lead vehicle 11 (row 1, column 2) and behind the first group (row 4, column 1). According to one embodiment, the processor 110 may determine the second formation by considering the positions of the other vehicles 20 in the second lane 42, thereby changing the second formation to a formation suitable for real-time traffic conditions.

[0095] Although not shown, if the number of other vehicles 20 is greater than or equal to the number of vehicles included in the first group (e.g., the lead vehicle 11, the first vehicle 12-1, and the second vehicle 12-2), then when the second group moves behind the other vehicles 20, the second formation after the change may spread out wider than the first formation before the change. If the second formation spreads out wider than the first formation, the cluster may easily separate, so the processor 110 may maintain the first formation. According to one embodiment, the processor 110 may maintain the first formation based on recognizing that the second lane 42 is not vacant and is occupied by other vehicles 20.

[0096] pass Figure 3 、 Figure 4 、 Figure 5 and Figure 6 The example described is based on the assumption that the second lane 42 includes only one lane. However, the operation for changing the formation can be performed even if the second lane 42 includes multiple lanes (for example, Figure 8 The following will describe an exemplary operation of changing the formation when the second lane 42 includes multiple lanes.

[0097] Figure 7 1 is a flowchart illustrating the operation of an electronic device for changing a cluster formation when a second lane includes a plurality of lane markings. Figure 8 The schematic shows the Figure 7 The operation is a process of changing the cluster formation.

[0098] Figure 7 The operations described in the may be in memory (e.g., Figure 2 Instructions stored in the memory 120 in the processor (e.g., Figure 2 When executed by the processor 110 in the electronic device (for example, Figure 2 In the following description, the operation is performed by the electronic device 100 in the lead vehicle 11 (for example, Figure 1The electronic device 100 configured in the leading vehicle 11 of FIG. 1 is referred to as the first electronic device 700 a , and the electronic device 100 configured in the following vehicle (eg, Figure 1 The other electronic devices 200 configured in the subsequent vehicle 12) are respectively referred to as a second electronic device 700b and a third electronic device 700c.

[0099] According to one embodiment, when the second lane 42 includes multiple lanes (e.g., Figure 8 When the vehicle 12 is in the third lane 43 and the fourth lane 44 in the second formation, the first electronic device 700a may classify at least a portion of the following vehicles 12 into a plurality of third groups (e.g., a fourth group and a fifth group). For example, the fourth group may be referred to as the group of the first following vehicle 740 that moves to the third lane 43 when changing to the second formation, and the fifth group may be referred to as the group of the second following vehicle 750 that moves to the fourth lane 44 when changing to the second formation. The second electronic device 700b may be an electronic device configured in the first following vehicle 740, and the third electronic device 700c may be an electronic device configured in the second following vehicle 750. The second electronic device 700b and / or the third electronic device 700c may be one or more.

[0100] refer to Figure 7 In operation 701 , the first electronic device 700 a may identify first information related to a first formation of vehicles 10 forming a cluster.

[0101] Figure 7 The operation 701 described in the Figure 3 For example, when the processor 110 executes the instructions stored in the memory 120, it can obtain first information related to the first formation of the vehicles 10. The first formation can be referred to as the cluster formation before the change. Figure 8 800a shows the vehicles 10 traveling in a cluster in a formation before the change, that is, a first formation. Figure 8 In 800a, when the vehicle 10 includes a leading vehicle 11 and five following vehicles 12 (e.g., a first vehicle 12-1, a second vehicle 12-2, a third vehicle 12-3, a fourth vehicle 12-4, and / or a fifth vehicle 12-5), the first formation may be composed of 6 rows and 1 column (e.g., 6×1). Figure 8 , the second lane 42 may include a third lane 43 and a fourth lane 44 .

[0102] In operation 702 , the first electronic device 700 a may acquire first environmental information related to the surrounding environment of the lead vehicle 11 .

[0103] Figure 7 Operation 702 described in the Figure 3For example, when the processor 110 executes the instructions stored in the memory 120, the processor 110 may perform the following operations based on the data from the camera (e.g., Figure 2 The first electronic device 700a can obtain first environmental information related to the surrounding environment of the lead vehicle 11 from an image obtained by the camera 140. For example, the first electronic device 700a can use a neural network model pre-trained for external object recognition to identify external objects such as other vehicles 20, lane markings 30, lanes 40, and / or traffic lights 50 around the lead vehicle 11 from images corresponding to external objects.

[0104] In operation 703 , the second electronic device 700 b and the third electronic device 700 c may respectively obtain second environmental information and third environmental information related to the surrounding environment of the subsequent vehicle 12 .

[0105] Figure 7 Operation 703 described in the Figure 3 For example, the processor (e.g., Figure 2 The processor 210) executes the memory (eg, Figure 2 When the instructions stored in the memory 220 of the device are stored, the user can perform the operation based on the instructions from the camera (for example, Figure 2 The second electronic device 700b can obtain second environmental information related to the surrounding environment of each subsequent vehicle 12 based on images obtained by the camera 240. For example, the second electronic device 700b can obtain information about the surrounding environment of the first subsequent vehicle 740 (e.g., second environmental information), and the third electronic device 700c can obtain information about the surrounding environment of the second subsequent vehicle 750 (e.g., third environmental information).

[0106] In operation 704 , the first electronic device 700 a may receive second environmental information and third environmental information related to the surrounding environment of the subsequent vehicle 12 from the subsequent vehicle 12 .

[0107] Figure 7 Operation 704 described in the Figure 3 For example, when executing the instructions stored in the memory 120 , the processor 110 may receive, through the wireless communication device 130 , the data including the second environment information and the data including the third environment information sent by the wireless communication device 230 .

[0108] In operation 705 , the first electronic device 700 a may acquire second information related to whether at least a portion of a second lane 42 including a plurality of lanes is vacant.

[0109] For example, when executing the instructions stored in the memory 120, the processor 110 may obtain second information related to whether at least a portion of the third lane 43 and at least a portion of the fourth lane 44 are vacant based on the first environment information, the second environment information, and the third environment information. Figure 8 , the third lane 43 may be a lane adjacent to the first lane 41, and the fourth lane 44 may be a lane adjacent to the third lane 43. For example, when the first formation consists of 6 rows and 1 column and is traveling in the left lane, the third lane 43 may be the middle lane, and the fourth lane 44 may be the right lane.

[0110] According to one embodiment, the first electronic device 700a may obtain second information related to whether at least a portion of the third lane 43 and at least a portion of the fourth lane 44 are vacant based on the first, second, and third environmental information. For example, when another vehicle 20 is not located in the third lane 43 and the fourth lane 44, images captured by the cameras 140 and 240 do not contain images corresponding to the other vehicle 20. Therefore, the first electronic device 700a may obtain second information indicating that the third lane 43 and the fourth lane 44 are vacant based on the first and second environmental information.

[0111] In operation 706 , the first electronic device 700 a may determine a second formation including a plurality of rows.

[0112] For example, when executing instructions stored in the memory 120, the processor 110 can determine a second formation based on the identification of a second lane 42 that includes multiple lanes that are at least partially vacant, where the second formation includes a first column corresponding to the first lane 41 and multiple second columns (rows) corresponding to the multiple lanes respectively.

[0113] Figure 8 800c shows the vehicles 10 traveling in a cluster in a changed formation, namely a second formation. According to one embodiment, the first electronic device 700a can determine the second formation based on the number of lanes. For example, when the second lane 42 includes two lanes (e.g., the third lane 43 and the fourth lane 44), the processor 110 can determine a second formation that includes multiple columns corresponding to the first lane 41, the third lane 43, and the fourth lane 44. The second formation can include a first column and multiple second columns (e.g., a third column and a fourth column). The first column can correspond to the first lane 41. The third column can correspond to the third lane 43. The fourth column can correspond to the fourth lane 44.

[0114] In operation 707 , the first electronic device 700 a may divide the vehicles 10 into a first group including the leading vehicle 11 and a plurality of third groups including only the following vehicles 12 based on the second formation.

[0115] For example, when executing instructions stored in the memory 120 , the processor 110 may divide the vehicles 10 into a plurality of groups based on the second formation in order to change the formation.

[0116] For example, the vehicles 10 constituting the cluster include 6 vehicles, and when the second formation includes 3 columns (e.g., the first column, the third column, and the fourth column), the processor 110 may classify the vehicles 10 into groups of 2 and assign them to the first group, the fourth group, and the fifth group. Figure 8 In 800c, when the second formation includes three columns (eg, the first column, the third column, and the fourth column), the processor 110 may divide the vehicles 10 into three groups corresponding to the three columns, respectively.

[0117] For example, the processor 110 may divide the vehicles 10 into a first group, a fourth group, and a fifth group. The first group, as a vehicle group corresponding to the first column of the second formation to be changed, may include the lead vehicle 11. The first group may be a vehicle group that maintains driving in the first lane 41. The fourth group may be a group of at least one subsequent vehicle (e.g., the first subsequent vehicle 740) corresponding to the third column of the second formation to be changed. The fourth group may include the first subsequent vehicle 740 that moves from the first lane 41 to the third lane 43. The fifth group may be a group of at least one subsequent vehicle (e.g., the second subsequent vehicle 750) corresponding to the fourth column of the second formation to be changed. The fifth group may include the second subsequent vehicle 750 that moves from the first lane 41 to the fourth lane 44. Figure 8 , the first vehicle 12 - 1 may be included in the first group. The first subsequent vehicle 740 included in the fourth group may include the second vehicle 12 - 2 and the third vehicle 12 - 3. The second subsequent vehicle 750 included in the fifth group may include the fourth vehicle 12 - 4 and the fifth vehicle 12 - 5.

[0118] In operation 708 , the first electronic device 700 a may transmit a first signal to the first subsequent vehicle 740 to move the first subsequent vehicle 740 to the third lane 43 .

[0119] For example, when executing instructions stored in the memory 120, the processor 110 may use the wireless communication device 130 to transmit a signal for controlling the travel of the first subsequent vehicle 740 to the first subsequent vehicle 740, thereby moving the first subsequent vehicles 740 (e.g., the second vehicle 12-2 and the third vehicle 12-3) included in the fourth group to the fourth lane 44. As described above, the data packet of the first signal may include a target identifier for indicating the first subsequent vehicle 740 that will receive the first signal. The second electronic device 700b may receive the first signal from the first electronic device 700a.

[0120] In operation 709 , the second electronic device 700 b may control the first subsequent vehicle 740 based on receiving the first signal to move the first subsequent vehicle 740 from the first lane 41 to the third lane 43 . Figure 8 800b shows the process of changing from the first formation to the second formation. Figure 8 As shown in FIG800b , the first following vehicle 740 may move from the first lane 41 to the third lane 43. As the first following vehicle 740 moves to the third lane 43, the first formation may change. While the first following vehicle 740 moves to the third lane 43, the processor 110 may control the second following vehicle 750 to remain in the first lane 41. For example, when waiting for a signal from the traffic light 50, when the first following vehicle 740 moves to the third lane 43, the second following vehicle 750 may remain in the waiting position without moving.

[0121] In operation 710 , the first electronic device 700 a may transmit a second signal for moving the second subsequent vehicle 750 to the fourth lane 44 to the second subsequent vehicle 750 .

[0122] For example, when executing instructions stored in the memory 120, the processor 110 may use the wireless communication device 130 to transmit a signal for controlling the travel of the second subsequent vehicle 750 to the second subsequent vehicle 750, thereby moving the second subsequent vehicles 750 (e.g., the fourth vehicle 12-4 and the fifth vehicle 12-5) included in the fifth group to the fourth lane 44. As described above, the data packet of the second signal may include a target identifier for indicating the second subsequent vehicle 750 that will receive the second signal. The third electronic device 700c may receive the second signal from the first electronic device 700a.

[0123] According to one embodiment, the processor 110 may be configured to send a second signal to the second following vehicle 750 after the first following vehicle 740 completes its movement. For example, the second electronic device 700b may send information regarding the surrounding environment of the first following vehicle 740, i.e., second environmental information, to the first electronic device 700a while the first following vehicle 740 is moving from the first lane 41 to the third lane 43. Upon completion of the movement of the first following vehicle 740 to the third lane 43, the second electronic device 700b may send a signal to the first electronic device 700a indicating completion of the movement. When the first following vehicle 740 is in the third lane 43, the processor 110 may determine whether the fourth lane 44 adjacent to the third lane 43 is vacant based on the second environmental information received from the second electronic device 700b. Upon identifying that the fourth lane 44 is at least partially vacant, the processor 110 may send a second signal to move the second following vehicle 750 to the fourth lane 44.

[0124] In operation 711, the third electronic device 700c may control the second subsequent vehicle 750 to move the second subsequent vehicle 750 from the first lane 41 to the fourth lane 44 based on receiving the second signal. Figure 8As shown in FIG800c, the second following vehicle 750 may move from the first lane 41 to the fourth lane 44. As the second following vehicle 750 moves to the fourth lane 44, the first formation may be changed to the second formation.

[0125] According to one embodiment, the processor 110 may determine a second formation based on the number of movable lanes, so that the vehicles 10 can travel in a cluster in a formation that prevents cluster separation. Figure 4 In the example shown, when the second lane 42 includes only one lane, the processor 110 may determine a second formation including two columns. Figure 8 In the example shown, when the second lane 42 includes multiple lanes, the processor 110 can determine a second formation including multiple columns (e.g., a first column and multiple second columns) corresponding to the multiple lanes. According to one embodiment, since the first electronic device 700a can change the cluster according to road conditions, cluster separation can be reduced during driving.

[0126] Figure 9 The process of changing the cluster formation is schematically shown.

[0127] Reference Figure 6 and Figure 7 The above description is substantially applicable even when the second lane 42 includes a plurality of lanes.

[0128] Reference Figure 9 When the second lane 42 includes multiple lanes (e.g., the third lane 43 and the fourth lane 44), the vehicles 10 can be divided into a first group (11, 12-1) including the lead vehicle 11, a fourth group (12-2, 12-3) including the first following vehicle 740, and a fifth group (12-4, 12-5) including the second following vehicle 750. When the cluster formation changes from the first formation to the second formation, the first group is located in the first lane 41, the fourth group moves from the first lane 41 to the third lane 43, and the fifth group moves from the first lane 41 to the fourth lane 44.

[0129] Figure 9 900a shows the vehicles 10 traveling in a cluster in a formation before the change, that is, a first formation. Figure 9 900b shows the process of changing from the first formation to the second formation. Figure 9 900a and 900b, the processor (e.g., Figure 2 The processor 110 may control the second subsequent vehicle 750 to be located in the first lane 41 by preventing the second subsequent vehicle 750 from moving while the first subsequent vehicle 740 is moving. The processor 110 may receive second environmental information about the environment surrounding the first subsequent vehicle 740 from the first subsequent vehicle 740, and identify whether the fourth lane 44 is vacant based on the second environmental information.

[0130] For example, after the first subsequent vehicle 740 moves to the third lane 43 and before the second subsequent vehicle 750 moves to the fourth lane 44, another vehicle 20 may move next to the first subsequent vehicle 740. In this case, the second electronic device 700b of the first subsequent vehicle 740 may use a camera (e.g., Figure 2 The second electronic device 700b may use the camera 240 of the first electronic device 700a to capture an image containing the other vehicle 20 and identify the other vehicle 20 from the image. The second electronic device 700b may transmit second environmental information indicating that the other vehicle 20 is located in the fourth lane 44 to the first electronic device 700a. Based on the second environmental information, the processor 110 may divide the fourth lane 44 into a first area 910 and a second area 920. For example, the first area 910 may be a vacant portion of the fourth lane 44, i.e., an area of ​​the fourth lane 44 not occupied by the other vehicle 20. For example, the second area 920 may be another portion of the fourth lane 44 occupied by the other vehicle 20, i.e., an area of ​​the fourth lane 44 that is not vacant.

[0131] According to one embodiment, the processor 110 may recognize the presence of another vehicle 20 in the fourth lane 44 based on the second environmental information received from the second electronic device 700b configured in the first following vehicle 740 located in the third lane 43. According to one embodiment, when the processor 110 executes the program stored in the memory (e.g., Figure 2 When the instructions in the memory 120 are read, the second formation for moving the fifth group to the first area 910 of the fourth lane 44 can be re-determined based on the first area 910 and the second area 920.

[0132] Figure 9 900c shows the vehicles 10 traveling in a cluster in a changed formation, ie, a second formation. Figure 9 In 900c, the processor 110 may redefine the second formation so that the fifth group moves to the first area 910 not occupied by other vehicles 20. For example, the second formation may be determined so that the following vehicles included in the fifth group (e.g., the fourth vehicle 12-4 and the fifth vehicle 12-5) are located behind the other vehicles 20 in the fourth lane 44. For example, although the second formation may be composed of 3 rows and 3 columns (e.g., 3×3), the vehicle 10 may not be located in front of the fourth vehicle 12-4 (row 1, column 3) and behind the first group (row 3, column 1). According to one embodiment, the processor 110 may determine the second formation by considering the positions of other vehicles 20 on the road, thereby changing the second formation to a formation suitable for real-time traffic conditions.

[0133] Figure 10a 、 Figure 10b and Figure 10cAn example of a process in which an electronic device generates a local map based on environmental information received from a vehicle according to an embodiment is shown.

[0134] According to one embodiment, the electronic device 100 may obtain environmental information (e.g., first environmental information) related to the surrounding environment of the leading vehicle 11 through the camera 140. The second electronic device 200 may obtain environmental information (e.g., second environmental information) related to the surrounding environment of the following vehicle 12 through the camera 240. The second electronic device 200 may send data containing the second environmental information to the first electronic device 100. According to one embodiment, the electronic device 100 may generate a local map representing the surrounding environment of the cluster-traveling vehicles 10 based on the first environmental information and the second environmental information, and determine the second formation based on the local map. The local map may be referenced as a map of the surrounding environment of the cluster-traveling vehicles 10.

[0135] Figure 10a The vehicles 10 are shown traveling in a group in a formation before the change, that is, a first formation.

[0136] Reference Figure 10a , the vehicles 10 traveling in a first formation group including 1 column may be located on a first lane 1011. Each vehicle 10 may utilize a camera (e.g., Figure 2 camera 140) or a camera (e.g., Figure 2 The camera 240 of the vehicle 10 captures an image of the surrounding environment, and by identifying external objects (e.g., other vehicles 20, lane lines 30, vehicles 40, and / or signal lights 50) from the image, information related to the surrounding environment is obtained. For example, the second electronic device 200 configured in the subsequent vehicle 12 may send second environmental information related to the surrounding environment of the subsequent vehicle 12 to the first electronic device 100. The first electronic device 100 may generate a local map of the spatial surrounding environment of the vehicle 10 by combining the first environmental information and the second environmental information. For example, the local map may include the current position of the vehicle 10 and information about external objects around the vehicle 10. The current position information of the vehicle 10 may be obtained through a GPS sensor (e.g., Figure 2 GPS sensor 150) and / or GPS sensor (e.g., Figure 2 GPS sensor 250) for identification.

[0137] According to one embodiment, the local map may include real-time environmental information. For example, the first environmental information and the second environmental information may be acquired in real time, and the electronic device 100 may generate a local map representing the real-time situation based on the acquired first and second environmental information. For example, the local map may include information representing other vehicles 20 located next to the lead vehicle 11.

[0138] Figure 10b The process of changing from the first formation to the second formation is shown. Figure 10b To change the cluster formation to the second formation, the second vehicle 12-2 and the third vehicle 12-3 may move to the second lane 1012 adjacent to the first lane 1011. For example, the cluster formation may change while waiting for a signal from traffic light 50. While the second vehicle 12-2 and the third vehicle 12-3 are moving, the fourth vehicle 12-4 and the fifth vehicle 12-5 may remain stationary in the first lane 1011. After the second vehicle 12-2 and the third vehicle 12-3 move to the second lane 1012, the second vehicle 12-2 and the third vehicle 12-3 may become adjacent to the third lane 1013. The camera 240 of the second vehicle 2-2 may capture images of the area surrounding the second vehicle 12-2, and the camera 240 of the third vehicle 12-3 may capture images of the area surrounding the third vehicle 12-3. For example, based on the movements of the second vehicle 12-2 and the third vehicle 12-3, the second environmental information provided in real time may include information related to the third lane 1013. For example, information related to the third lane 1013 may be referred to as information indicating whether the third lane 1013 is vacant or occupied by another vehicle.

[0139] Figure 10c The vehicle 10 is shown traveling in a group in a changed formation, that is, a second formation. Figure 10c , the fourth vehicle 12-4 and the fifth vehicle 12-5 move to the third lane 1013, and the cluster formation changes from the first formation to the second formation. The third lane 1013 may be adjacent to the fourth lane 1014. The camera 240 of the fourth vehicle 12-4 may capture images of the area surrounding the fourth vehicle 12-4, and the camera 240 of the fifth vehicle 12-5 may capture images of the area surrounding the fifth vehicle 12-5. According to one embodiment, the second environmental information provided in real time may include information related to the fourth lane 1014 based on the movement of the fourth vehicle 12-4 and the fifth vehicle 12-5.

[0140] According to one embodiment, because the second environmental information acquired in real time may vary depending on the swarm formation, the processor 110 may generate a local map based on the second environmental information acquired in real time. The local map may represent information about the environment surrounding the swarm vehicles 10, and the processor 110 may use this information to determine the swarm formation or control the movement of the swarm vehicles 10. Because the local map, which reflects real-time information, can reflect various possible road conditions, the processor 110 may control swarm movement based on the local map.

[0141] Figure 11 Another example of a process in which an electronic device generates a local map based on environmental information received from a vehicle according to an embodiment is shown.

[0142] Figure 111100a is a group of vehicles 10 traveling in the formation before the change, that is, the first formation. Figure 11 1100b shows the process of changing from the first formation to the second formation. Figure 11 1100c shows the vehicles 10 traveling in a cluster in a changed formation, ie, a second formation.

[0143] Reference Figure 11 1100a, since the first formation 1130 includes one column, the first environment information and the second environment information may be difficult to accurately reflect the situation of the third lane 1013 spaced apart from the first lane 1011. Figure 11 In 1100b, as the second vehicle 12-2 and the third vehicle 12-3 move into the second lane 1012, the camera 240 of the second vehicle 12-2 may capture images of the area surrounding the second vehicle 12-2, and the camera 240 of the third vehicle 12-3 may capture images of the area surrounding the third vehicle 12-3. For example, based on the movement of the second vehicle 12-2 and the third vehicle 12-3, the second environmental information provided in real time may include information related to the other vehicle 20 in the third lane 1013. The local map generated in real time may also include information related to the other vehicle 20 in the fourth lane 1014.

[0144] Reference Figure 11 In 1100c, second formation 1150 may be determined based on real-time conditions. For example, because other vehicles 20 are present in third lane 1013, processor 110 may determine second formation 1150 such that fourth vehicle 12-4 and fifth vehicle 12-5 are positioned behind lead vehicle 11. According to one embodiment, since a local map can display real-time road information, the swarm formation can be appropriately modified based on this real-time road information. According to one embodiment, electronic device 100 may utilize a neural network model to control swarm driving using a formation adapted to real-time information.

[0145] According to one embodiment, the electronic device 100 can reduce the situation where the cluster is separated due to signal waiting by appropriately changing the cluster formation. When the vehicles 10 set off after waiting for the signal, by changing the cluster formation, the vehicles 10 can set off more quickly.

[0146] Figure 12 An example block diagram of an autonomous driving system for a vehicle according to an embodiment is shown.

[0147] according to Figure 12, the vehicle automatic driving system 1200 can be a deep learning network including a sensor 1203, an image preprocessor 1205, a deep learning network 1207, an artificial intelligence (AI) processor 1209, a vehicle control module 1211, a network interface 1213 and a communication unit 1215. In various embodiments, the various components can be connected through different interfaces. For example, the sensor data sensed and output by the sensor 1203 can be fed to the image preprocessor 1205. The sensor data processed by the image preprocessor 1205 can be fed to the deep learning network 1207 run by the AI ​​processor 1209. The output of the deep learning network 1207 run by the AI ​​processor 1209 can be fed to the vehicle control module 1211. The intermediate results of the deep learning network 1207 running on the AI ​​processor 1209 can be fed to the AI ​​processor 1209. In various embodiments, the network interface 1213 can be connected to the in-vehicle electronic devices (for example, Figure 2 The electronic device 100 and / or other electronic device 200 in the autonomous driving control system 1200 communicates with the autonomous driving path information and / or autonomous driving control instructions for the autonomous driving of the vehicle to the internal module. In one embodiment, the network interface 1213 can be used to transmit sensor data obtained by the sensor 1203 to an external server. In some embodiments, the autonomous driving control system 1200 may include additional or fewer components as appropriate. For example, in some embodiments, the image preprocessor 1205 may be an optional component. For another example, a post-processing module (not shown) may be included in the autonomous driving control system 1200 to perform post-processing on the output of the deep learning network 1207 before providing the output to the vehicle control module 1211.

[0148] In some embodiments, sensor 1203 may include more than one sensor. In various embodiments, sensor 1203 may be installed at different locations on the vehicle. Sensor 1203 may face one or more different directions. For example, sensor 1203 may be installed on the front, sides, rear, and / or roof of the vehicle, facing forward, rear, or sideways, among other directions. In some embodiments, sensor 1203 may be an image sensor, such as a high dynamic range camera. In some embodiments, sensor 1203 may include non-visual sensors. In some embodiments, sensor 1203 may include radar, laser radar (LiDAR), and / or ultrasonic sensors in addition to image sensors. In some embodiments, sensor 1203 is not mounted on the vehicle having vehicle control module 1211. For example, sensor 1203 may be part of a deep learning system to capture sensor data and may be installed in the environment or on a road, and / or installed on surrounding vehicles.

[0149] In some embodiments, the image pre-processor 1205 can be used to pre-process the sensor data of the sensor 1203. For example, the image pre-processor 1205 can be used to pre-process the sensor data, split the sensor data into one or more constituent elements, and / or post-process one or more constituent elements. In some embodiments, the image pre-processor 1205 can be a graphics processing unit (GPU), a central processing unit (CPU), an image signal processor, or a specialized image processor. In various embodiments, the image pre-processor 1205 can be a tone mapping processor for processing high dynamic range data. In some embodiments, the image pre-processor 1205 can be a component of the AI ​​processor 1209.

[0150] In some embodiments, the deep learning network 1207 may be a deep learning network for implementing control instructions for controlling the autonomous vehicle. For example, the deep learning network 1207 may be an artificial neural network, such as a convolutional neural network (CNN), trained using sensor data, and the output of the deep learning network 1207 is provided to the vehicle control module 1211.

[0151] In some embodiments, the artificial intelligence (AI) processor 1209 may be a hardware processor for running the deep learning network 1207. In some embodiments, the AI ​​processor 1209 may be a specialized AI processor for performing inference on sensor data using a convolutional neural network (CNN). In some embodiments, the AI ​​processor 1209 may be optimized for the bit depth of the sensor data. In some embodiments, the AI ​​processor 1209 may be optimized for deep learning operations (such as operations in a neural network including convolution, inner product, vector and / or matrix operations). In some embodiments, the AI ​​processor 1209 may be implemented using multiple graphics processing units (GPUs) to efficiently perform parallel processing.

[0152] In various embodiments, the AI ​​processor 1209 can be coupled to a memory storing instructions via an input / output interface. When executed by the AI ​​processor 1209, the instructions can perform deep learning analysis on sensor data from the sensor 1203 and generate machine learning results for enabling at least partially autonomous operation of the vehicle. In certain embodiments, the vehicle control module 1211 can process vehicle control instructions output by the artificial intelligence (AI) processor 1209 and translate the output of the AI ​​processor 1209 into instructions for controlling various modules of the vehicle. In certain embodiments, the vehicle control module 1211 can be used to control the vehicle to achieve autonomous driving. In certain embodiments, the vehicle control module 1211 can adjust the steering and / or speed of the vehicle. For example, the vehicle control module 1211 can be used to control the vehicle's driving, including operations such as deceleration, acceleration, steering, lane changing, and lane keeping. In some embodiments, the vehicle control module 1211 can generate control signals for controlling vehicle lighting, such as brake lights, turn signals, and headlights. In some embodiments, the vehicle control module 1211 can be used to control vehicle audio-related systems, such as the vehicle's sound system, the vehicle's audio warnings, the vehicle's microphone system, and the vehicle's horn system.

[0153] In some embodiments, the vehicle control module 1211 can be used to control notification systems, including warning systems for alerting passengers and / or drivers to driving events, such as approaching a predetermined destination or a potential collision. In some embodiments, the vehicle control module 1211 can be used to adjust vehicle sensors, such as sensor 1203. For example, the vehicle control module 1211 can modify the orientation of sensor 1203, change the output resolution and / or format type of sensor 1203, increase or decrease the capture rate, adjust the dynamic range, and adjust the focus of the camera. In addition, the vehicle control module 1211 can turn the operation of sensors on or off individually or collectively.

[0154] In certain embodiments, the vehicle control module 1211 can be used to modify parameters of the image preprocessor 1205, such as adjusting the frequency range of the filter, adjusting edge detection parameters for feature and / or object detection, or adjusting channels and bit depth. In various embodiments, the vehicle control module 1211 can be used to control autonomous driving functions and / or driver assistance functions of the vehicle.

[0155] In certain embodiments, the network interface 1213 may serve as an internal interface between modules of the autonomous driving control system 1200 and the communication unit 1215. Specifically, the network interface 1213 may serve as a communication interface for receiving and / or transmitting data, including voice data. In various embodiments, the network interface 1213 may connect to an external server via the communication unit 1215 to facilitate voice call connections, receive and / or transmit text messages, transmit sensor data, and update the vehicle's software to the autonomous driving system or the vehicle's autonomous driving system software.

[0156] In various embodiments, the communication unit 1215 may include a variety of wireless interfaces such as cellular or WiFi. For example, the network interface 1213 may be connected to an external server via the communication unit 1215 to receive updates on operating parameters and / or instructions for the sensor 1203, the image preprocessor 1205, the deep learning network 1207, the AI ​​processor 1209, and the vehicle control module 1211. For example, the machine learning model of the deep learning network 1207 may be updated via the communication unit 1215. In another example, the communication unit 1215 may be used to update operating parameters (such as image processing parameters) of the image preprocessor 1205 and / or the firmware of the sensor 1203.

[0157] In other embodiments, the communication unit 1215 can be used to activate communications with emergency services and emergency contacts in the event of an accident or near-accident. For example, in the event of a collision, the communication unit 1215 can be used to call emergency services for assistance and to notify emergency services of details of the collision and the location of the vehicle. In various embodiments, the communication unit 1215 can also be used to update or obtain an estimated time of arrival and / or destination location.

[0158] According to one embodiment, Figure 12The illustrated autonomous driving system 1200 may be comprised of the vehicle's electronic device 100. According to one embodiment, when a user triggers an autonomous driving release event during the vehicle's autonomous driving process, the AI ​​processor 1209 of the autonomous driving system 1200 may control the vehicle's autonomous driving software to learn by inputting information related to the autonomous driving release event into the training set data of a deep learning network.

[0159] Figure 13 and Figure 14 An example block diagram of an autonomous driving mobile body according to an embodiment is shown. Figure 15 An example of a gateway is shown in relation to a user device in various embodiments.

[0160] Reference Figure 13 According to this embodiment, the autonomous driving mobile body 1300 may include a control device 1400, perception modules 1304a, 1304b, 1304c, 1304d, an engine 1306 and a user interface 1308.

[0161] The autonomous vehicle 1300 may have an autonomous driving mode or a manual mode. For example, the vehicle may be switched from the manual mode to the autonomous driving mode or vice versa based on user input received through the user interface 1308.

[0162] When the moving object 1300 operates in the autonomous driving mode, the autonomous driving moving object 1300 may operate under the control of the control device 1400 .

[0163] In this embodiment, the control device 1400 may include a controller 1420 having a memory 1422 and a processor 1424 , a sensor 1410 , a communication device 1430 , and an object detection device 1440 .

[0164] The object detection device 1440 may perform all or part of the functions of the distance measurement device.

[0165] That is, in this embodiment, the object detection device 1440 is a device for detecting an object located outside the moving body 1300. The object detection device 1440 can detect an object located outside the moving body 1300 and generate object information based on the detection result.

[0166] The object information may include information on the presence or absence of the object, position information of the object, distance information between the moving body and the object, and relative speed information between the moving body and the object.

[0167] Objects may include lane markings, other vehicles, pedestrians, traffic signals, light, roads, structures, speed bumps, terrain features, animals, and other objects located outside of the mobile object 1300. Traffic signals may include traffic lights, traffic signs, and patterns or text painted on the road surface. Furthermore, light may be generated by lights equipped by other vehicles, light generated by streetlights, or sunlight.

[0168] Furthermore, structures can be objects located around the road and fixed to the ground. For example, structures can include streetlights, roadside trees, buildings, utility poles, traffic lights, and bridges. Terrain objects can include mountains and hills.

[0169] The object detection device 1440 may include a camera module. The controller 1420 may extract object information from an external image captured by the camera module and process the information related thereto.

[0170] Furthermore, object detection device 1440 may also include an imaging device for sensing the external environment. In addition to LIDAR, RADAR, GPS devices, odometry and other computer vision devices, ultrasonic sensors, and infrared sensors may also be used. These devices can be used selectively or simultaneously as needed to achieve more accurate detection.

[0171] On the other hand, according to an embodiment of the present invention, the distance measuring device can calculate the distance between the autonomous driving mobile body 1300 and the object, and in conjunction with the control device 1400 of the autonomous driving mobile body 1300, control the operation of the mobile body based on the calculated distance.

[0172] For example, when the distance between the autonomous vehicle 1300 and an object is likely to conflict, the autonomous vehicle 1300 can control the brakes to reduce speed or stop. Another example is that when the object is moving, the autonomous vehicle 1300 can control its speed to maintain a predetermined distance from the object.

[0173] According to an embodiment of the present invention, such a distance measurement device may be configured as a module in the control device 1400 of the autonomous driving mobile body 1300. In other words, the memory 1422 and processor 1424 of the control device 1400 may implement the anti-collision method of the present invention in software.

[0174] In addition, the sensor 1410 can be connected to the sensing modules 1304a, 1304b, 1304c, and 1304d to obtain various sensing information of the internal / external environment of the mobile object. The sensor 1410 may include a posture sensor (e.g., a yaw sensor, a roll sensor, a pitch sensor), a collision sensor, a wheel sensor, a speed sensor, a tilt sensor, a weight detection sensor, a heading sensor, a gyro sensor, a position module, a mobile forward / backward sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor detected by steering wheel rotation, a mobile internal temperature sensor, a mobile internal humidity sensor, an ultrasonic sensor, a light sensor, an accelerator pedal position sensor, a brake pedal position sensor, and the like.

[0175] Therefore, the sensor 1410 can obtain perception signals about the following: mobile body posture information, mobile body collision information, mobile body direction information, mobile body position information (GPS information), mobile body angle information, mobile body speed information, mobile body acceleration information, mobile body tilt information, mobile body forward / backward information, battery information, fuel information, tire information, mobile body light information, mobile body internal temperature information, mobile body internal humidity information, steering wheel rotation angle, mobile body external lighting, accelerator pedal pressure, and brake pedal pressure, etc.

[0176] In addition, sensor 1410 may also include other sensors, such as an accelerator pedal sensor, a pressure sensor, an engine speed sensor, an air flow sensor (AFS), an intake air temperature sensor (ATS), a water temperature sensor (WTS), a throttle position sensor (TPS), a TDC sensor, and a crankshaft angle sensor (CAS).

[0177] As described above, the sensor 1410 may generate mobile object state information based on the sensing data.

[0178] The wireless communication device 1430 is configured to implement wireless communication between the autonomous driving mobile bodies 1300. For example, the autonomous driving mobile body 1300 can communicate with a user's mobile phone, other wireless communication devices 1430, other mobile bodies, a central device (such as a traffic control device), a server, etc. The wireless communication device 1430 can send and receive wireless signals according to the access wireless protocol. The wireless communication protocol may include Wi-Fi, Bluetooth, Long-Term Evolution (LTE), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), and Global Systems for Mobile Communications (GSM), but is not limited to these protocols.

[0179] In addition, according to this embodiment, the autonomous driving mobile body 1300 can also achieve communication between mobile bodies through the wireless communication device 1430. That is, the wireless communication device 1430 can communicate with other mobile bodies and other vehicles on the road through vehicle-to-vehicle (V2V) communication. The autonomous driving mobile body 1300 can send and receive data such as driving warnings and traffic information through inter-vehicle communication, and can also request information from other mobile bodies or receive requests from other mobile bodies. For example, the wireless communication device 1430 can achieve V2V communication through a dedicated short-range communication (DSRC) device or a C-V2V (Cellular-V2V) device. In addition to inter-vehicle communication, communication between vehicles and other things (such as electronic devices carried by pedestrians, etc.) (V2X, Vehicle-to-Everything communication) can also be achieved through the wireless communication device 1430.

[0180] In addition, the wireless communication device 1430 can also obtain information from various mobile bodies (Mobility) such as infrastructure on the road (such as traffic lights, CCTV, RSU, eNode B, etc.) or other autonomous driving (Autonomous Driving) / non-autonomous driving (Non-AutonomousDriving) vehicles through a non-terrestrial network (Non-Terrestrial Network), and use it as the information required for the autonomous driving mobile body 1300 to perform autonomous driving.

[0181] For example, the wireless communication device 1430 can wirelessly communicate with the low Earth orbit (LEO) satellite system, medium Earth orbit (MEO) satellite system, geostationary orbit (GEO) satellite system, high altitude platform (HAP) system, etc. that constitute the non-terrestrial network through a non-terrestrial network dedicated antenna mounted on the autonomous driving mobile body 1300.

[0182] For example, the wireless communication device 1430 can conduct wireless communications with various platforms constituting the NTN based on the wireless access specifications of the 5G NR NTN (5th Generation New Radio Non-Terrestrial Network) standard specifications currently under discussion by organizations such as 3GPP, but is not limited thereto.

[0183] In this embodiment, the controller 1420 can consider various information such as the location, current time, available power, etc. of the autonomous driving mobile body 1300, select a platform that can appropriately perform NTN communication, and control the wireless communication device 1430 to perform wireless communication with the selected platform.

[0184] In this embodiment, controller 1420, which controls the overall operation of various units within mobile object 1300, can be configured by the mobile object's manufacturer during manufacturing or further configured after manufacturing to implement autonomous driving functions. Alternatively, it can include a configuration that continuously executes additional functions by upgrading the controller 1420 configured at manufacturing time. This type of controller 1420 may also be referred to as an ECU (Electronic Control Unit).

[0185] Controller 1420 can collect various data from connected sensors 1410, object detection device 1440, communication device 1430, etc., and based on the collected data, transmit control signals to other components of the mobile body, including sensors 1410, engine 1306, user interface 1308, communication device 1430, and object detection device 1440. In addition, although not specifically described, control signals can also be transmitted to an acceleration device, braking system, steering device, or navigation device related to the movement of the mobile body.

[0186] In this embodiment, the controller 1420 can control the engine 1306. For example, when the autonomous vehicle 1300 detects a speed limit on the road, the controller 1420 can control the engine 1306 to ensure that the driving speed does not exceed the speed limit, or to accelerate the driving speed of the autonomous vehicle 1300 within a range that does not exceed the speed limit.

[0187] Furthermore, when the autonomous vehicle 1300 approaches or deviates from a lane line during its travel, the controller 1420 can determine whether such approach or deviation constitutes a normal driving situation or another driving situation, and control the engine 1306 based on the determination to adjust the vehicle's travel. Specifically, the autonomous vehicle 1300 can detect lane lines formed on both sides of the lane in which the vehicle is traveling. In this case, the controller 1420 can determine whether the autonomous vehicle 1300 is approaching or deviating from a lane line. If it is determined that the autonomous vehicle 1300 is approaching or deviating from a lane line, the controller 1420 can further determine whether such movement is due to a normal driving situation or another driving situation. Here, as an example of a normal driving situation, the vehicle may need to change lanes. Alternatively, as an example of another driving situation, the vehicle may not need to change lanes. If the controller 1420 determines that the autonomous vehicle 1300 is approaching or deviating from a lane line when a lane change is not necessary, the controller 1420 can control the autonomous vehicle 1300 to travel normally in the relevant lane without deviating from the lane line.

[0188] When there are other moving objects or obstacles ahead of the moving object, the engine 1306 or the braking system can be controlled to decelerate the moving object. In addition to speed, the trajectory, driving path, and steering angle can also be controlled. Alternatively, the controller 1420 can generate the necessary control signals to control the moving object based on information about the moving object's lane, driving signals, and other external environments.

[0189] In addition to generating its own control signals, the controller 1420 can also communicate with surrounding mobile objects or a central server, and send commands to control surrounding devices through the received information, thereby controlling the travel of the mobile object.

[0190] Furthermore, if the position or viewing angle of the camera module 1450 changes, the controller 1420 may have difficulty accurately identifying the moving object or lane markings as in the present embodiment. To prevent this, the controller 1420 may also generate a control signal to calibrate the camera module 1450. Therefore, in this embodiment, the controller 1420 issues a calibration control signal to the camera module 1450. This ensures that the camera module 1450 maintains its normal installation position, orientation, and viewing angle even if the installation position of the camera module 1450 changes due to vibration or impact generated by the movement of the autonomous driving moving object 1300. The controller 1420 may generate a control signal to calibrate the camera module 1450 when a change exceeds a threshold between the pre-stored initial installation position, orientation, and viewing angle of the camera module 1450 and the initial installation position, orientation, and viewing angle of the camera module 1450 measured during driving of the autonomous driving moving object 1300.

[0191] In this embodiment, the controller 1420 may include a memory 1422 and a processor 1424. The processor 1424 may execute software stored in the memory 1422 in response to control signals from the controller 1420. Specifically, the controller 1420 stores data and commands required for executing the lane detection method described in the present invention in the memory 1422. These commands may be executed by the processor 1424 to implement one or more methods disclosed herein.

[0192] In this case, the memory 1422 may be stored on a recording medium executable by the non-volatile processor 1424. The memory 1422 may store software and data via appropriate internal or external devices. The memory 1422 may be composed of RAM (random access memory), ROM (read only memory), a hard disk, or a memory device connected to a dongle.

[0193] The memory 1422 can store at least an operating system (OS), user applications, and executable commands. The memory 1422 can also store application data and array data structures.

[0194] Processor 1424 may be a microprocessor or suitable electronic processor, and may be a controller, microcontroller, or state machine.

[0195] The processor 1424 may be implemented by a combination of computing devices, which may be composed of a digital signal processor, a microprocessor, or a suitable combination thereof.

[0196] On the other hand, the autonomous mobile object 1300 may also include a user interface 1308 for receiving user input to the control device 1400. The user interface 1308 allows the user to input information through an appropriate interactive method. For example, this may be implemented through a touch screen, keyboard, or operation buttons. The user interface 1308 transmits the input or command to the controller 1420, which then executes the control operation of the mobile object in response to the input or command.

[0197] In addition, the user interface 1308 can also communicate with devices outside the autonomous driving mobile body 1300 through the wireless communication device 1430. For example, the user interface 1308 can be linked with a mobile phone, tablet computer, or other computer device.

[0198] Furthermore, while the autonomously driven vehicle 1300 described in this embodiment includes an engine 1306, it may also include other types of propulsion systems. For example, the vehicle may be operated by electricity, hydrogen, or a hybrid system comprising a combination thereof. Therefore, the controller 1420 includes a propulsion mechanism specific to the propulsion system of the autonomously driven vehicle 1300 and may provide corresponding control signals to the components of each propulsion mechanism.

[0199] Below, refer to Figure 14 , further describing in detail the detailed structure of the control device 1400 according to this embodiment.

[0200] The control device 1400 includes a processor 1424. The processor 1424 can be a general-purpose single-chip or multi-chip microprocessor, a dedicated microprocessor, a microcontroller, a programmable gate array, etc. The processor can also be referred to as a central processing unit (CPU). In addition, in this embodiment, the processor 1424 can also be used as a combination of multiple processors.

[0201] The control device 1400 further includes a memory 1422. The memory 1422 may be any electronic component capable of storing electronic information. In addition to being a single memory, the memory 1422 may also include a combination of multiple memories 1422.

[0202] The data and command 1422a required for the distance measurement device according to the present invention to execute the distance measurement method may be stored in the memory 1422. When the processor 1424 executes the command 1422a, all or part of the command 1422a and the data 1422b required to execute the command may be loaded onto the processor 1424 (1424a, 1424b).

[0203] The control device 1400 may include a transmitter 1430a, a receiver 1430b, or a transceiver 1430c for allowing signal transmission and reception. One or more antennas 1432a, 1432b may be electrically connected to the transmitter 1430a, the receiver 1430b, or each transceiver 1430c, and may further include an antenna.

[0204] The control device 1400 may further include a digital signal processor (DSP) 1470. The DSP 1470 allows the mobile device to quickly process digital signals.

[0205] The control device 1400 may further include a communication interface 1480. The communication interface 1480 may include one or more ports and / or communication modules for connecting other devices to the control device 1400. The communication interface 1480 allows a user to interact with the control device 1400.

[0206] The various components of the control device 1400 can be connected via one or more buses 1490, and the buses 1490 can include a power bus, a control signal bus, a status signal bus, a data bus, etc. Under the control of the processor 1424, the components can communicate information with each other via the buses 1490 and perform predetermined functions.

[0207] On the other hand, in various embodiments, the control device 1400 can be associated with a gateway to communicate with the secure cloud. Figure 15 , the control device 1400 may be associated with a gateway 1505 for providing information acquired from at least one of the components (1501 to 1504) of the vehicle 1500 to a secure cloud 1506. For example, the gateway 1505 may be included in the control device 1400. As another example, the gateway 1505 may be configured as an independent device within the vehicle 1500, separate from the control device 1400. The gateway 1505 can connect the software management cloud 1509 and the secure cloud 1506, which have different networks, and the internal network of the vehicle 1500 protected by the in-vehicle security software 1510, to achieve communication.

[0208] For example, component 1501 may be a sensor. For example, the sensor may be used to obtain information about at least one of the state of vehicle 1500 or the state around vehicle 1500. For example, component 1501 may include sensor 1410.

[0209] For example, component 1502 may be an ECU (electronic control unit), which may be used for engine control, transmission control, airbag control, or tire pressure management.

[0210] For example, component 1503 may be an instrument cluster. For example, the instrument cluster may be a panel located in front of the driver's seat in a dashboard. For example, the instrument cluster may be configured to display information necessary for driving to the driver (or passenger). For example, the instrument cluster may display at least one of a visual element indicating engine revolutions per minute (RPM), a visual element indicating the speed of vehicle 1500, a visual element indicating the remaining fuel level, a visual element indicating the gear status, and a visual element indicating information obtained through component 1501.

[0211] For example, component 1504 may be a telematics device. For example, the telematics device may be a device that provides various mobile communication services such as location information and safe driving in a vehicle by combining wireless communication technology and GPS (global positioning system) technology. For example, the telematics device may be used to connect the vehicle 1500 to the driver, the cloud (e.g., the safety cloud 1506) and / or the surrounding environment. For example, the telematics device may support high bandwidth and low latency for 5G NR specification technology (e.g., 5G NR's V2X technology, 5G NR's NTN (Non-Terrestrial Network) technology). For example, the telematics device may support autonomous driving of the vehicle 1500.

[0212] For example, gateway 1505 can be used to connect the network within vehicle 1500 with a software management cloud 1509 and a security cloud 1506, which are external networks. For example, software management cloud 1509 can be used to update or manage at least one software required for driving and managing vehicle 1500. For example, software management cloud 1509 can be linked with in-car security software 1510 installed within the vehicle. For example, in-car security software 1510 can be used to provide security functions within vehicle 1500. For example, in-car security software 1510 can encrypt data sent and received via the in-car network using an encryption key obtained from an external authorized server to encrypt the in-vehicle network. In various embodiments, the encryption key used by in-vehicle security software 1510 can be generated in response to vehicle identification information (vehicle license plate, vehicle identification number (VIN)) or information uniquely assigned to each user (e.g., user identification information).

[0213] In various embodiments, the gateway 1505 can transmit data encrypted by the in-vehicle security software 1510 using the encryption key to the software management cloud 1509 and / or the security cloud 1506. The software management cloud 1509 and / or the security cloud 1506 decrypt the data encrypted by the in-vehicle security software 1510 using the encryption key using a decryption key capable of decrypting the data, thereby identifying the vehicle or user from which the data was received. For example, because the decryption key is a unique key corresponding to the encryption key, the software management cloud 1509 and / or the security cloud 1506 can identify the data sender (e.g., the vehicle or user) based on the data decrypted using the decryption key.

[0214] For example, gateway 1505 is configured to support in-vehicle security software 1510 and may be associated with control device 1400. For example, gateway 1505 may be associated with control device 1400 to support a connection between control device 1400 and client device 1507 connected to secure cloud 1506. According to another example, gateway 1505 may be associated with control device 1400 to support a connection between control device 1400 and a third-party cloud 1508 connected to secure cloud 1506. However, the present invention is not limited thereto.

[0215] In various embodiments, gateway 1505 can be used to connect vehicle 1500 with a software management cloud 1509 for managing the operating software of vehicle 1500. For example, software management cloud 1509 monitors whether the operating software of vehicle 1500 needs to be updated and, upon detecting that the operating software of vehicle 1500 needs to be updated, provides data for updating the operating software of vehicle 1500 via gateway 1505. In another example, software management cloud 1509 receives a user request for updating the operating software of vehicle 1500 from vehicle 1500 via gateway 1505 and, based on the request, provides data for updating the operating software of vehicle 1500. However, the present invention is not limited to this.

[0216] Figure 16 is a diagram for explaining an operation of an electronic device for training a neural network based on a training data set according to an embodiment.

[0217] Reference Figure 16 The described operations can be performed by the above-mentioned electronic devices (e.g., Figure 2 Executed by the electronic device 100).

[0218] refer to Figure 16In operation 1602, according to one embodiment, the electronic device may obtain a training data set. The electronic device may obtain a training data set for supervised learning. The training data may include input data and ground truth data pairs corresponding to the input data. The ground truth data may represent output data to be obtained from a neural network that receives input data of the ground truth data pair. The ground truth data may be obtained by the aforementioned electronic device.

[0219] For example, when training a neural network to recognize an image, the training data may include information about the image and one or more objects contained in the image. The information may include a category (category or class) of an object that can be recognized by the image. The information may include the position, width, height and / or size of a visual object corresponding to the object in the image. The training data set identified by operation 1602 may include multiple training data pairs. In the example of training a neural network to recognize an image, the training data set identified by the electronic device may include multiple images and ground truth data corresponding to each of the multiple images.

[0220] Reference Figure 16 In operation 1604, according to one embodiment, the electronic device may train the neural network based on the training data set. In one embodiment of training the neural network based on supervised learning, the electronic device may input the input data contained in the training data into the input layer of the neural network. Figure 17 An example of a neural network including the input layer is described. The electronic device can obtain output data of the neural network corresponding to the input data from the output layer of the neural network that receives the input data through the input layer.

[0221] In one embodiment, the training of operation 1604 may be performed based on the difference between the output data and the ground truth data corresponding to the input data included in the training data. For example, the electronic device may adjust one or more parameters related to the neural network (e.g., the reference Figure 17 The electronic device may adjust the one or more parameters to reduce the difference. The operation of the electronic device to adjust the one or more parameters may be referred to as tuning the neural network. The electronic device may perform neural network tuning based on the output data by using a function defined for evaluating the performance of the neural network, such as a cost function. The difference between the output data and the ground truth data may be included in an example of the cost function.

[0222] refer to Figure 16 In operation 1606, according to one embodiment, the electronic device may identify whether the neural network trained in operation 1604 outputs valid output data. Valid output data means that the difference (or cost function) between the output data and the ground truth data satisfies the conditions set for using the neural network. For example, when the average value and / or the maximum value of the difference between the output data and the ground truth data is less than or equal to a specified threshold, the electronic device may determine that the neural network outputs valid output data.

[0223] If the neural network does not output valid output data (1606-No), the electronic device may repeatedly perform training of the neural network based on operation 1604. The embodiment is not limited thereto, and the electronic device may repeatedly perform operations 1602 and 1604.

[0224] When valid output data is obtained from the neural network (1606-Yes), the electronic device according to one embodiment may use the trained neural network in operation 1608. For example, the electronic device may input other input data, which is different from the input data input to the neural network as training data, into the neural network. The electronic device may use the output data obtained from the neural network that received the other input data as the result of inference performed on the other input data by the neural network.

[0225] Figure 17 is a block diagram of an electronic device according to an embodiment.

[0226] Figure 17 The electronic device 100 may include the aforementioned electronic devices.

[0227] For example, refer to Figure 16 The described operation can be performed by Figure 17 The electronic device 100 and / or Figure 17 Executed by processor 1710.

[0228] Reference Figure 17, the processor 1710 of the electronic device 100 can perform computations related to the neural network 1730 stored in the memory 1720. The processor 1710 may include at least one of a central processing unit (CPU), a graphics processing unit (GPU), or a neural processing unit (NPU). The NPU can be implemented as a chip separate from the CPU, or integrated in the form of a system on a chip (SoC) on a chip such as a CPU. The NPU integrated in the CPU can be called a neural core and / or an artificial intelligence (AI) accelerator.

[0229] Reference Figure 17 , the processor 1710 may identify a neural network 1730 stored in the memory 1720. The neural network 1730 may include a combination of an input layer 1732, one or more hidden layers 1734 (or intermediate layers), and an output layer 1736. Each of the above layers (e.g., the input layer 1732, one or more hidden layers 1734, and the output layer 1736) may include multiple nodes. The number of hidden layers 1734 may vary depending on the embodiment, and a neural network 1730 including multiple hidden layers 1734 may be referred to as a deep neural network. The operation of training the deep neural network may be referred to as deep learning.

[0230] In one embodiment, if neural network 1730 has a feedforward neural network structure, a first node included in a particular layer may be connected to all second nodes included in other layers preceding the particular layer. Parameters stored for neural network 1730 in memory 1720 may include weights assigned to connections between the second nodes and the first node. In neural network 1730 having a feedforward neural network structure, the value of the first node may correspond to a weighted sum of values ​​assigned to the second node based on the weights assigned to the connections connecting the second node and the first node.

[0231] In one embodiment, if neural network 1730 has a convolutional neural network structure, a first node included in a specific layer may correspond to a weighted sum of some second nodes included in other layers before the specific layer. The part of the second nodes corresponding to the first node may be identified by a filter corresponding to the specific layer. Parameters stored in memory 1720 for neural network 1730 may include weights representing the filter. The filter may include one or more nodes in the second node for calculating the weighted sum of the first node, and weights corresponding to each of the one or more nodes.

[0232] According to one embodiment, the processor 1710 of the electronic device 100 may train the neural network 1730 using the training data set 1740 stored in the memory 1720. Based on the training data set 1740, the processor 1710 may execute a reference Figure 16 The described operations thereby adjust one or more parameters stored in memory 1720 for neural network 1730.

[0233] According to one embodiment, the processor 1710 of the electronic device 100 can perform object detection, object recognition, and / or object classification using a neural network 1730 trained based on a training data set 1740. The processor 1710 can input an image (or video) acquired through the camera 1750 into the input layer 1732 of the neural network 1730. Based on the input layer 1732 into which the image is input, the processor 1710 can sequentially acquire the values ​​of the nodes of each layer included in the neural network 1730, thereby acquiring a set of values ​​(e.g., output data) of the nodes of the output layer 1736. The output data can be used as the result of reasoning about the information contained in the image using the neural network 1730. The embodiment is not limited thereto, and the processor 1710 can also acquire an image (or video) from an external electronic device connected to the electronic device 100 via the communication circuit 1760 and input it into the neural network 1730.

[0234] In one embodiment, the neural network 1730 trained to process an image can be used to identify regions corresponding to objects in the image (object detection) and / or identify the categories of objects represented in the image (object recognition and / or object classification). For example, the electronic device 100 can use the neural network 1730 to segment regions corresponding to the object in the image based on rectangular shapes, such as bounding boxes. For example, the electronic device 100 can use the neural network 1730 to identify at least one category matching the object from a plurality of specified categories.

[0235] Figure 18a and Figure 18bAn example of a vehicle is shown.

[0236] The aforementioned vehicles that perform cluster driving may refer to conventional trucks.

[0237] Over the years, the trucking industry has experienced sustained growth and expanded its service offerings to address more complex supply chains. These services include last-mile deliveries, drop-trailer programs, and intermodal transportation through ports (a form of transportation in which freight is delivered to its destination using two or more different modes of transportation, such as ship and rail, or ship and aircraft).

[0238] As a result, due to the extremely diverse modes of cargo transportation, manufacturers of freight equipment have designed different forms of equipment to transport cargo according to various transportation needs.

[0239] In this specification, a truck that tows a trailer whose main purpose is to transport (carry or cater) freight will be generally referred to as a tractor.

[0240] The tractors described in this specification can be divided into conventional trucks (or bonneted trucks), cab-over trucks (or cab-over engine trucks), and semi-conventional trucks, which are between conventional trucks and cab-over trucks, based on the position and shape of their cabs.

[0241] In a conventional truck, the engine and hood are located above the front axle in front of the tractor cab, with the driver sitting behind the front axle. This type of tractor, with the engine located in front of the driver, is primarily used in North America.

[0242] In contrast, a cab-over truck has the cab at the front of the tractor, with the driver seated in front of the front axle. The front of the tractor is flat, often called a "flat face" or "flat nose," with the engine positioned below the driver. This type of tractor is primarily used in most countries in Europe and Asia.

[0243] Just as tractors come in many forms depending on their purpose and needs, the trailers towed by tractors also come in a variety of styles. The most representative trailer types include full-trailers and semi-trailers. The difference between full-trailers and semi-trailers lies in whether the trailer has both a front axle and a rear axle. These trailers can be connected to a box truck or tractor using a coupling device.

[0244] Specifically, a full-trailer is a commercial freight trailer equipped with a front axle and a rear axle. Designed to carry its total weight independently of a towing vehicle, a full-trailer is equipped with a drawbar for connecting to a hauling unit or towing unit, such as a tractor. This type of trailer is widely used in the United States, Canada, and other regions.

[0245] In contrast, a semi-trailer is a cargo trailer equipped with only a rear axle and no front axle. A large portion of its weight is supported by a tractor vehicle connected to it by a hitch called a fifth wheel. When the semi-trailer is detached from the tractor vehicle and stationary, the weight of the trailer can be supported by the landing gear mounted on the bottom of the semi-trailer, which is vertically extended to the ground. The combination of a semi-trailer and a tractor vehicle is called a semi-trailer truck, and in the United States it is often referred to as a "semi-trailer", "tractor-trailer", "semi-truck", "big rig" or "semi". The "fifth wheel" mentioned above refers to a horizontal wheel mounted on the axle of a tractor truck to facilitate steering of the trailer. It is also called the fifth wheel. A "fifth wheel" is a device used to achieve a movable connection (movable connection) between the tractor and the semi-trailer. It typically consists of a trunnion plate and a locking device that securely fastens the kingpin mounted on the semi-trailer to the trunnion plate on the tractor.

[0246] In this specification, the following terms will be used based on the above-mentioned tractor / trailer. For convenience of explanation, a "trailer" refers to a cargo transport vehicle connected to a tractor for a trailer, and a "tractor" refers to the towing vehicle used to move the trailer. Furthermore, to minimize limitations on the scope of the present invention due to the embodiments described in the detailed description, a tractor hauling / towing a "trailer" may be described as a "towing vehicle," and a trailer towed by the tractor may be described as a "towed vehicle." These terms may be used interchangeably in the description.

[0247] In addition, for the convenience of explanation, it is preferred that the “trailer” mentioned in this specification be understood to refer to a “semi-trailer”, but not limited thereto.

[0248] This manual Figure 18a and Figure 18b Although the trailer shown is in the form of a "semi-trailer", this is only for the convenience of explanation and it should not be understood that the embodiments of the present invention are only applicable to the "semi-trailer" form.

[0249] Reference Figure 18a and Figure 18b, a vehicle 1800 including a tractor or tractor unit 1810 and a semi-trailer 1820 is exemplarily shown. Figure 18a Indicates the state where the tractor 1810 and the semi-trailer 1820 are not connected. Figure 18b The diagram shows the state in which the tractor 1810 is connected to the semi-trailer 1820. In one embodiment, the semi-trailer 1820 can be selectively connected via a steering wheel hook 1860 on the tractor 1810. The steering wheel hook 1860 can be connected to a kingpin 1880 fixed to the semi-trailer 1820 in a known manner. The vehicle 1800 including the tractor 1810 and the semi-trailer 1820 can be referred to as a truck. The vehicle 1800 can also include only the tractor 1810. Figure 18a and Figure 18b The semi-trailer 1820 is shown in the form of a "semi-trailer", but this is only for the convenience of explanation and should not be understood as the embodiment of the present disclosure is only applicable to the form of a "semi-trailer". Figure 18a and Figure 18b The tractor 1810 is shown in the form of a "cab-over truck", but this is only for the convenience of explanation and it should not be understood that the embodiments of the present disclosure are only applicable to the form of a "cab-over truck".

[0250] In one embodiment, the tractor 1810 may include a front portion 1811 and a rear portion 1812. The front portion 1811 may include a cab (or cabin) in which the driver sits. The rear portion 1812 may be provided with a steering wheel hook 1860 for connecting to the semi-trailer 1820. In one embodiment, the semi-trailer 1820 may include a kingpin 1880 connected to the steering wheel hook 1860 of the tractor 1810, and support legs 1890 for supporting the semi-trailer 1820 on the ground when the semi-trailer 1820 is not connected to the tractor 1810. The kingpin 1880 and the support legs 1890 may be mounted on the bottom of the semi-trailer 1820.

[0251] In one embodiment, the tractor 1810 may include an internal combustion engine, an electric motor, or a combination of the two, which is referred to as an engine. The tractor 1810 may include a battery and / or a fuel tank (e.g., a fuel tank for storing gasoline, diesel, liquefied natural gas (LNG), liquefied petroleum gas (LPG), and / or hydrogen). For example, the tractor 1810 may include a rechargeable battery and an electric motor driven by the electrical energy stored in the battery. Such a tractor may be referred to as an electric vehicle (EV) and / or an electric truck. For example, the tractor 1810 may include not only a battery and an electric motor but also a fuel tank and an engine. Such a tractor may be referred to as a hybrid vehicle (e.g., a plug-in hybrid electric vehicle (PHEV)).

[0252] In one embodiment, the semi-trailer 1820 can be coupled to or detached from the tractor 1810. For example, the semi-trailer 1820 can be coupled to the rear portion 1812 of the tractor 1810. The semi-trailer 1820 coupled to the tractor 1810 can be towed by the tractor 1810. To facilitate travel on curved roads, the semi-trailer 1820 can be rotationally coupled to the tractor 1810. For example, the tractor 1810 and the semi-trailer 1820 can be rotationally coupled via a coupling device including a steering wheel hook 1860 and a towing pin 1880. However, the connection mechanism between the tractor 1810 and the semi-trailer 1820 is not limited thereto.

[0253] An electronic device for platooning vehicles is provided. According to one embodiment, the electronic device may include a processor and a memory for storing instructions. When the instructions are executed by the processor, the electronic device may be capable of: identifying first information related to a first formation of the vehicles; obtaining second information related to whether a second lane, which is distinct from the first lane in which the vehicles are located, is at least partially vacant while the vehicles are controlled in the first formation; determining a second formation based on the second information so as to move a portion of the vehicles to the second lane; dividing the vehicles into a first group including a leading vehicle and a second group including only following vehicles based on the second formation; and sending a signal to the following vehicles included in the second group for moving the second group to the second lane.

[0254] According to an embodiment, the electronic device may further include a camera. The second information may include: first environmental information related to the surrounding environment of the leading vehicle acquired by the camera; and second environmental information related to the surrounding environment of the following vehicle received from the following vehicle.

[0255] According to one embodiment, when the instructions are executed by the processor, the electronic device can: determine the second formation based on identifying the second lane including a plurality of lanes that are at least partially vacant, the second formation including a first row corresponding to the first lane and a plurality of second rows corresponding to the plurality of lanes respectively; based on the second formation, divide the vehicles into the first group and a plurality of third groups corresponding to the plurality of second rows respectively; and send signals to subsequent vehicles included in the plurality of third groups for moving the plurality of third groups to the plurality of lanes.

[0256] According to one embodiment, the plurality of lanes may include a third lane and a fourth lane. The plurality of second columns may include a third column corresponding to the third lane and a fourth column corresponding to the fourth lane. The plurality of third groups may include a fourth group including a first subsequent vehicle moving from the first lane to the third lane, and a fifth group including a second subsequent vehicle moving from the first lane to the fourth lane.

[0257] According to one embodiment, when the instructions are executed by the processor, the electronic device can: send a first signal to the first subsequent vehicle to move the first subsequent vehicle from the first lane to the third lane; control the second subsequent vehicle included in the fifth group so that the second subsequent vehicle is located in the first lane during the period when the first subsequent vehicle moves from the first lane to the third lane; and send a second signal to the second subsequent vehicle based on the completion of the movement of the first subsequent vehicle to the third lane to move the second subsequent vehicle from the first lane to the fourth lane.

[0258] According to one embodiment, when the instructions are executed by the processor, the electronic device can: receive third information related to whether the fourth lane is at least partially vacant from the first subsequent vehicle during the process of the first subsequent vehicle moving from the first lane to the third lane; and send the second signal to the second subsequent vehicle based on the third information.

[0259] According to one embodiment, when the instructions are executed by the processor, the electronic device can: based on the third information, send the second signal to the second following vehicle when it is identified that the fourth lane is at least partially vacant; based on the third information, stop sending the second signal when it is identified that the fourth lane is at least partially occupied by other vehicles.

[0260] According to one embodiment, when the instructions are executed by the processor, the electronic device may be enabled to: send a signal for moving the second group to the second lane to change the first formation to the second formation while the vehicles are waiting for a stop signal at a traffic light.

[0261] According to one embodiment, when the instructions are executed by the processor, the electronic device can: obtain first time information related to the remaining time of the stop signal; calculate second time information related to the time required to change the first formation to the second formation; and send a signal for moving the second group to the second lane based on the second time information being shorter than the first time information.

[0262] According to one embodiment, when the instructions are executed by the processor, the electronic device can: identify, based on the second information, a first area of ​​the second lane that is at least partially vacant and a second area of ​​the second lane that is occupied by other vehicles; and determine, based on the first area and the second area, the second formation so that the second group moves to the first area of ​​the second lane.

[0263] According to one embodiment, the number of rows in the second formation may be greater than that in the first formation.

[0264] According to one embodiment, when the instructions are executed by the processor, the electronic device may be enabled to: generate a local map representing information about the vehicle's surrounding environment based on the second information.

[0265] According to one embodiment, when the instructions are executed by the processor, the electronic device can be enabled to: obtain the second information using a neural network model and determine the second formation.

[0266] A method performed by an electronic device is provided. According to one embodiment, the method of the electronic device may include: identifying first information related to a first formation of clustered vehicles. The method may include: obtaining second information related to whether a second lane, which is distinguished from the first lane in which the vehicles are located, is at least partially vacant during the period when the vehicles are controlled in the first formation. The method may include: determining a second formation based on the second information so that a portion of the vehicles move to the second lane. The method may include: distinguishing the vehicles into a first group including a leading vehicle and a second group including only following vehicles based on the second formation. The method may include: sending a signal to the following vehicles included in the second group for moving the second group to the second lane.

[0267] According to one embodiment, the method may further include: determining a second formation based on identifying a second lane including a plurality of lanes that are at least partially vacant, the second formation including a first row corresponding to the first lane and a plurality of second rows corresponding to the plurality of lanes. The method may further include: dividing the vehicles into the first group and a plurality of third groups corresponding to the plurality of second rows based on the second formation. The method may further include: sending a signal to subsequent vehicles included in the plurality of third groups to move the plurality of third groups to the plurality of lanes.

[0268] According to an embodiment, the method may further include: while the vehicles are waiting for a stop signal at a traffic light, sending a signal for moving the second group to the second lane, so as to change the first formation into the second formation.

[0269] According to one embodiment, the method may further include: obtaining first time information related to the remaining time of the stop signal. The method may further include: calculating second time information related to the time required to change the first formation to the second formation. The method may further include: transmitting a signal for moving the second group to the second lane based on the second time information being shorter than the first time information.

[0270] According to one embodiment, the method may further include: identifying, based on the second information, a first area of ​​the second lane that is at least partially vacant and a second area of ​​the second lane that is occupied by other vehicles. The method may further include: determining the second formation based on the first area and the second area, so as to move the second group to the first area of ​​the second lane.

[0271] According to an embodiment, the method may further include: generating a local map representing the vehicle surrounding environment information based on the second information.

[0272] According to one embodiment, the method may further include: acquiring the second information using a neural network model, and determining the second formation.

Claims

1. An electronic device for cluster driving of vehicles, comprising: processor; as well as Memory for storing instructions, When the instructions are executed by the processor, the electronic device is enabled to: identifying first information associated with a first formation of vehicles; While the vehicles are controlled in the first formation, obtaining second information related to whether a second lane distinct from the first lane in which the vehicles are located is at least partially vacant; determining a second formation based on the second information so as to move a portion of the vehicles to the second lane; Based on the second formation, the vehicles are divided into a first group including a leading vehicle and a second group including only following vehicles; as well as A signal for moving the second group to the second lane is sent to the following vehicles included in the second group.

2. The electronic device according to claim 1, wherein Also includes a camera, The second information includes: first environmental information related to the surrounding environment of the leading vehicle acquired by the camera; and Second environmental information related to an environment surrounding the following vehicle is received from the following vehicle.

3. The electronic device according to claim 1, wherein: When the instructions are executed by the processor, the electronic device is enabled to: determining a second formation based on identifying the second lane including a plurality of lanes that are at least partially vacant, the second formation including a first column corresponding to the first lane and a plurality of second columns corresponding to the plurality of lanes, respectively; Based on the second formation, the vehicles are divided into the first group and a plurality of third groups corresponding to the plurality of second columns, respectively; Signals for moving the plurality of third groups to the plurality of lanes are transmitted to subsequent vehicles included in the plurality of third groups.

4. The electronic device according to claim 3, wherein: The plurality of lanes include: a third lane, and a fourth lane, The plurality of second columns include: a third column corresponding to the third lane, and a fourth column corresponding to the fourth lane, The plurality of third groups include a fourth group including a first subsequent vehicle moving from the first lane to the third lane, and a fifth group including a second subsequent vehicle moving from the first lane to the fourth lane.

5. The electronic device according to claim 4, wherein: When the instructions are executed by the processor, the electronic device is enabled to: sending a first signal to the first following vehicle to move the first following vehicle from the first lane to the third lane, During the period when the first subsequent vehicle moves from the first lane to the third lane, controlling the second subsequent vehicle included in the fifth group so that the second subsequent vehicle is located in the first lane, Based on completion of movement of the first subsequent vehicle to the third lane, a second signal is sent to the second subsequent vehicle to move the second subsequent vehicle from the first lane to the fourth lane.

6. The electronic device according to claim 5, wherein: When the instructions are executed by the processor, the electronic device is enabled to: receiving, from the first following vehicle during movement of the first lane to the third lane, third information regarding whether the fourth lane is at least partially vacant, Based on the third information, the second signal is sent to the second subsequent vehicle.

7. The electronic device according to claim 6, wherein: When the instructions are executed by the processor, the electronic device is enabled to: Based on the third information, when it is recognized that the fourth lane is at least partially vacant, sending the second signal to the second following vehicle; Based on the third information, when it is recognized that at least part of the fourth lane is occupied by another vehicle, sending of the second signal is stopped.

8. The electronic device according to claim 1, wherein: When the instructions are executed by the processor, the electronic device is enabled to: While the vehicles are waiting for a stop signal at a traffic light, a signal for moving the second group to the second lane is sent to change the first formation into the second formation.

9. The electronic device according to claim 8, wherein: When the instructions are executed by the processor, the electronic device is enabled to: Acquire first time information related to the remaining time of the stop signal, calculating second time information related to the time required to change the first formation into the second formation, Based on the second time information being shorter than the first time information, a signal for moving the second group to the second lane is transmitted.

10. The electronic device according to claim 1, wherein: When the instructions are executed by the processor, the electronic device is enabled to: identifying, based on the second information, a first area of ​​the second lane that is at least partially vacant and a second area of ​​the second lane that is occupied by another vehicle, Based on the first area and the second area, the second formation is determined so that the second group moves to the first area of ​​the second lane.

11. The electronic device according to claim 1, wherein: The second formation has more columns than the first formation.

12. The electronic device according to claim 1, wherein: When the instructions are executed by the processor, the electronic device is enabled to: Based on the second information, a local map representing information about the vehicle's surroundings is generated.

13. The electronic device according to claim 1, wherein: When the instructions are executed by the processor, the electronic device is enabled to: The second information is acquired using a neural network model, and the second formation is determined.

14. A method of an electronic device, comprising the following operations: identifying first information related to a first formation of clustered vehicles; While the vehicles are controlled in the first formation, obtaining second information related to whether a second lane distinct from the first lane in which the vehicles are located is at least partially vacant; determining a second formation based on the second information so as to move a portion of the vehicles to the second lane; Based on the second formation, the vehicles are divided into a first group including a leading vehicle and a second group including only following vehicles; as well as A signal for moving the second group to the second lane is sent to the following vehicles included in the second group.

15. The method according to claim 14, wherein It also includes the following operations: determining, based on identifying the second lane including a plurality of lanes that are at least partially vacant, a second formation including a first column corresponding to the first lane and a plurality of second columns corresponding to the plurality of lanes, respectively; Based on the second formation, the vehicles are divided into the first group and a plurality of third groups corresponding to the plurality of second columns respectively; as well as Signals for moving the plurality of third groups to the plurality of lanes are transmitted to subsequent vehicles included in the plurality of third groups.

16. The method according to claim 14, wherein It also includes the following operations: While the vehicles are waiting for a stop signal at a traffic light, a signal for moving the second group to the second lane is sent to change the first formation into the second formation.

17. The method according to claim 16, wherein It also includes the following operations: Acquiring first time information related to a remaining time of the stop signal; calculating second time information related to a time required to change the first formation into the second formation; as well as Based on the second time information being shorter than the first time information, a signal for moving the second group to the second lane is transmitted.

18. The method according to claim 14, wherein It also includes the following operations: identifying, based on the second information, a first region of the second lane that is at least partially vacant and a second region of the second lane that is occupied by another vehicle; and Based on the first area and the second area, the second formation is determined so that the second group moves to the first area of ​​the second lane.

19. The method according to claim 14, wherein It also includes the following operations: Based on the second information, a local map representing information about the vehicle's surroundings is generated.

20. The method of claim 14, wherein: It also includes the following operations: The second information is acquired using a neural network model, and the second formation is determined.

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