Electronic device and method for controlling cluster traveling vehicle

Abstract

The invention relates to an electronic device and method for controlling a cluster traveling vehicle. The electronic device comprises a camera; a memory to store instructions; and a processor. When the instructions are executed by the processor, the electronic device can obtain curvature information of a path to be entered by the vehicle executing cluster driving; on the basis of the curvature information, identifying a curve section with the curvature greater than or equal to a reference curvature on the path; acquiring, from each of the vehicles, vehicle-related information including at least one of a weight of the vehicle or a length of the vehicle; obtaining respective risk degree related information of the vehicles when the vehicles run on the curve road section by using the vehicle related information obtained from the vehicles and the curvature of the curve road section; based on the risk degree related information, determining the driving speed of the vehicle on the curve road section; and transmitting the determined speeds to the vehicles, respectively.

Description

Technical Field

[0001] The present disclosure relates to an electronic device and method for controlling cluster-traveling vehicles. Background Art

[0002] Swarming is a technology for autonomous driving that controls two or more vehicles. The vehicles in a platoon can operate in a certain formation. By reducing the distance between vehicles, platooning reduces air resistance, thereby improving fuel efficiency and reducing the risk of accidents. It also regulates traffic flow and reduces congestion. The vehicles in a platoon can include a leading vehicle and following vehicles.

[0003] The above information may be used as background art to help understand the present disclosure. No part of the above content is claimed or identified as prior art that can be applied to the present disclosure. Summary of the Invention

[0004] According to one embodiment, an electronic device for platooning vehicles may include: a camera; a memory for storing instructions; and a processor. The instructions, when executed by the processor, enable the electronic device to obtain curvature information of a path that the vehicles performing platooning are about to enter. The instructions, when executed by the processor, enable the electronic device to identify, based on the curvature information, a curved section on the path whose curvature is greater than or equal to a reference curvature. The instructions, when executed by the processor, enable the electronic device to obtain vehicle-related information, including at least one of vehicle weight or vehicle length, from each vehicle. The instructions, when executed by the processor, enable the electronic device to utilize the vehicle-related information obtained from each vehicle and the curvature of the curved section to obtain risk-related information for each vehicle when traversing the curved section. The instructions, when executed by the processor, enable the electronic device to determine, based on the risk-related information, a speed for each vehicle to travel on the curved section. The instructions, when executed by the processor, enable the electronic device to transmit the determined speed to each vehicle.

[0005] According to one embodiment, a method for an electronic device for cluster driving of vehicles may include the following operations: obtaining curvature information of a path that the vehicles performing cluster driving are about to enter. The method may include the following operations: identifying a curved section on the path whose curvature is greater than or equal to a reference curvature based on the curvature information. The method may include the following operations: obtaining vehicle-related information including at least one of the weight of the vehicle or the length of the vehicle from each of the vehicles. The method may include the following operations: obtaining risk-related information of each of the vehicles when driving on the curved section using the vehicle-related information obtained from each of the vehicles and the curvature of the curved section. The method may include the following operations: determining the speed of the vehicle for driving on the curved section based on the risk-related information. The method may include the following operations: sending the determined speed to each of the vehicles.

[0006] According to one embodiment, a non-transitory computer-readable storage medium may store one or more programs. The one or more programs may include instructions that, when executed by a processor of an electronic device, are capable of obtaining curvature information of a path that vehicles performing cluster driving are about to enter. The one or more programs may include instructions that, when executed by the processor of the electronic device, are capable of identifying, based on the curvature information, curved sections on the path whose curvature is greater than or equal to a reference curvature. The one or more programs may include instructions that, when executed by the processor of the electronic device, are capable of obtaining vehicle-related information, including at least one of vehicle weight or vehicle length, from each of the vehicles. The one or more programs may include instructions that, when executed by the processor of the electronic device, are capable of using the vehicle-related information obtained from each of the vehicles and the curvature of the curved section to obtain risk-related information for each of the vehicles when traveling on the curved section. The one or more programs may include instructions that, when executed by the processor of the electronic device, are capable of determining, based on the risk-related information, a speed for the vehicle to travel on the curved section. The one or more programs may include instructions that, when executed by the processor of the electronic device, are capable of transmitting the determined speed to each of the vehicles.

[0007] In one embodiment, an electronic device can control clustered driving of vehicles. The electronic device can change the speed and / or movement path of each vehicle on a curved section of a clustered driving route. By changing the speed and / or movement path of each vehicle on a curved section, the electronic device can prevent accidents (e.g., rollovers or collisions). BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1Platooning vehicles are schematically shown.

[0009] Figure 2 is a block diagram of an electronic device for cluster travel of vehicles according to one embodiment.

[0010] Figure 3a A flowchart illustrating the operation of an electronic device according to one embodiment is shown.

[0011] Figure 3b An example of an operation of an electronic device for acquiring curvature information of a path that a vehicle is about to enter according to an embodiment is shown.

[0012] Figure 4 A signal flow diagram related to the operation of an electronic device and other electronic devices according to one embodiment is shown.

[0013] Figure 5 FIG. 1 shows a formation of vehicles before entering a curved road section according to one embodiment.

[0014] Figure 6 An example of an operation of an electronic device for identifying a curved road section according to an embodiment is shown.

[0015] Figure 7a and Figure 7b An example of operation of an electronic device for controlling a vehicle to pass through a curved road section according to an embodiment is shown.

[0016] Figure 8a and Figure 8b An example of operation of an electronic device for controlling a vehicle to pass through a curved road section according to an embodiment is shown.

[0017] Figure 9a and Figure 9b An example of operation of an electronic device for controlling a vehicle to pass through a curved road section according to an embodiment is shown.

[0018] Figure 10a and Figure 10b An operation example of an electronic device for controlling a vehicle to pass through a curved road section according to an embodiment is shown.

[0019] Figure 10c and Figure 10d An example of a conventional truck is shown.

[0020] Figure 11 An example block diagram of an autonomous driving system for a vehicle according to one embodiment is shown.

[0021] Figure 12 and Figure 13An example block diagram of an autonomous driving mobile body according to one embodiment is shown.

[0022] Figure 14 An example of a gateway in relation to a user device according to various embodiments is shown.

[0023] Figure 15 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.

[0024] Figure 16 is a block diagram of an electronic device according to one embodiment. DETAILED DESCRIPTION

[0025] Hereinafter, the embodiments of the present invention will be described with reference to the accompanying drawings. In the description of the accompanying drawings, similar or related components may be denoted by similar reference numerals.

[0026] Figure 1 Platooning vehicles are schematically shown.

[0027] 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 may share control information of the vehicle 10 and information collected by the electronic devices 100 and 200 disposed on the vehicle 10 in real time using wireless communication technology. 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).

[0028] Vehicles 10 can be divided into a leading vehicle 11 and following vehicles 12. Leading vehicle 11 can be defined as the vehicle at the front of the group of vehicles 10 on the driving path, 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 at the front of the group, electronic device 100 can obtain more information than other electronic devices 200.

[0029] According to one embodiment, each vehicle 10 can be configured based on a different shape. For example, the shape of each vehicle 10 can be determined based on the vehicle model. Vehicle models may include, but are not limited to, sedans, sports cars, military vehicles, trucks, buses, motorcycles, and forklifts. For example, the lead vehicle 11 can be configured in the shape of a sedan (e.g., a sedan). Vehicle 15 in the following vehicle group 12 can be configured in the shape of a truck comprising a tractor and a trailer. Vehicles 16 and 17 in the following vehicle group 12 can be configured in the shape of sedans.

[0030] The electronic device 100 can transmit and / or receive data with an external electronic device (e.g., a base station 33, a satellite 34, and / or a server 35). For example, the electronic device 100 can transmit and / or receive data with the server 35 via at least one of the base station 33 and / or the satellite 34.

[0031] For example, the electronic device 100 can receive data containing information related to the driving route from an external electronic device (e.g., base station 33, satellite 34 and / or server 35) to determine the driving route, and can also send data containing information related to the real-time location of the cluster to an external electronic device (e.g., base station 33, satellite 34 and / or server 35).

[0032] According to an embodiment, the base station 33 and / or the server 35 may be configured to manage group driving within a specified area. For example, the base station 33 and / or the server 35 may be configured to manage vehicle driving (or group driving) within a cell defined based on coverage. Each time a vehicle switches to another cell, it may be controlled by a different base station and / or server. Each time a cell switches, the vehicle may establish a connection with a different base station (e.g., a handover).

[0033] According to one embodiment, the electronic device 100 may control the driving of the vehicles 10 in the cluster based on information related to the vehicles 10 in the cluster (e.g., the driving route, driving speed, the distance between the vehicles 10, and / or the formation of the cluster) and / or information related to the surrounding environment (e.g., road conditions, other vehicles 20, lane lines 30, and / or lanes 40 including lanes 41 and 42). For example, the electronic device 100 may transmit a signal for controlling the driving of the cluster to the other electronic devices 200 disposed in each subsequent vehicle 12. The other electronic devices 200 may then control the driving of the subsequent vehicles 12 based on the signal received from the electronic device 100.

[0034] The vehicles 10 may include a variety of different vehicle models. The travel path of the vehicles 10 performing cluster driving may include at least one curved section. Because the vehicles 10 include vehicles of different models, each vehicle 10 may have a different length or weight. Due to the different lengths or weights of the vehicles 10, the required travel speed and / or movement path for each vehicle 10 to safely navigate the curved section may be different. Therefore, if the speed of the vehicle 10 is maintained on a curved section while maintaining the speed of a straight section, the probability of an accident involving the vehicle 10 may increase. The following description will describe the technical features for controlling the vehicles 10 performing cluster driving.

[0035] The following describes an electronic device 100 for controlling vehicles 10 performing group driving 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, "first lane" describes the lane in which vehicles 10 maintain their formation before entering a curved road section and does not refer to a lane near the center line as defined by regulations.

[0036] Figure 2 FIG. 1 is a block diagram of an electronic device for cluster travel of vehicles according to one embodiment.

[0037] Reference Figure 2 According to one embodiment, the electronic device 100 may include a processor 110, a memory 120, a wireless communication device 130, a camera 140, a GPS (Global Positioning System) sensor 150 and / or a wired communication device 160. According to one embodiment, the electronic device 100 may be referred to as being disposed on a leading vehicle (e.g., Figure 1 An electronic device within a vehicle 11).

[0038] For example, the processor 110, the memory 120, the wireless communication device 130, the camera 140, the GPS sensor 150, and / or the wired communication device 160 may be electrically and / or operatively coupled with 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 wireless means, such that a first hardware component in the hardware can control a second hardware component.

[0039] exist Figure 2 In the figure, the processor 110, the memory 120, the camera 140, the wireless communication device 130, the GPS sensor 150 and / or the wired communication device 160 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 or a single package, such as a SoC (system on a chip).

[0040] 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 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.

[0041] 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., lane markings)) based on the neural network model stored in the memory 120. Figure 1 Lane lines 30), lanes (e.g. Figure 1 Lane 40), other vehicles (e.g. Figure 1 other vehicles 20) and / or signal lights (e.g. Figure 1 The signal light 50)).

[0042] 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., a base station)) via the wireless communication device 130. Figure 1 Base stations 33 in), satellites (e.g. Figure 1 Satellite 34 in) and / or server (e.g.: Figure 1 The wireless communication device 130 may be electrically connected to an antenna (e.g., a server 35) for transmitting and / or receiving signals) and other electronic devices 200. Figure 13The 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 an external electronic device through the wireless communication device 130. The electronic device 100 can send information for controlling subsequent vehicles (for example: Figure 1 The other electronic devices 200 may receive the signal via the wireless communication device 230.

[0043] 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 an 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 that are 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.

[0044] According to one embodiment, the electronic device 100 can identify the environment around the leading vehicle 11 through the camera 140. For example, the electronic device 100 can identify 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 identify 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 other vehicles 20 traveling on a lane 42 in the image is obtained, and other vehicles 20 on the lane 42 are identified from the image.

[0045] According to one embodiment, the wired communication device 160 may be used to connect the electronic device 100 to the control circuitry (e.g., an electronic control unit (ECU)) of the lead vehicle 11. For example, the electronic device 100 may transmit a signal for controlling the lead vehicle 11 to the control circuitry of the lead vehicle 11 via the wired communication device 160. The electronic device 100 may control the lead vehicle 11 via the control circuitry connected to the wired communication device 160.

[0046] 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, a GPS sensor 250, and / or a wired communication device 260. The description of the components of the electronic device 100 may be substantially the same as the components of the other electronic devices 200.

[0047] 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.

[0048] According to one embodiment, the electronic device 100 can change the formation using a neural network model. For example, the processor 110 can decide whether to change the formation based on information related to 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, in a moving path for cluster driving, if the traffic flow is not smooth, the processor 110 can change the formation or driving mode (e.g., speed, moving path) for cluster driving. In addition, the processor 110 can also change the formation or driving mode of cluster driving according to the status of the vehicle 10 (e.g., the remaining fuel amount, tire pressure, or engine oil pressure).

[0049] Figure 3a A flowchart illustrating the operation of an electronic device according to one embodiment is shown.

[0050] Figure 3b An example of an operation of an electronic device for acquiring curvature information of a path that a vehicle is about to enter according to an embodiment is shown.

[0051] In the following embodiments, each operation can be performed in sequence, but does not necessarily have to be performed in sequence. For example, the order of each operation can be changed, or at least two operations can be performed in parallel.

[0052] refer to Figure 3a In operation 310, the processor 110 of the electronic device 100 may obtain curvature information of a path that the vehicles 10 performing cluster driving are about to enter. For example, the electronic device 100 may be configured to control cluster driving of the vehicles 10. For example, the electronic device 100 may be included in the leading vehicle 11 of the vehicles 10 performing cluster driving.

[0053] According to one embodiment, the processor 110 may determine a travel path for the vehicles 10 performing tethered driving. For example, the processor 110 may set a destination for the vehicles 10 performing tethered driving. Based on the destination of the vehicles 10 performing tethered driving, the processor 110 may use an electronic map to identify candidate routes that are traversable within the shortest distance and / or shortest time. The processor 110 may determine one of the candidate routes as the route for the vehicles 10 performing tethered driving.

[0054] For example, processor 110 may identify information about a route that vehicle 10 performing tethered driving is about to enter based on an electronic map. For example, the electronic map may be provided by a map application or a navigation application. The route information may include at least one of the following: road class (e.g., highway, ordinary road), road type (e.g., ordinary road, elevated road, underpass), number of lanes, lane width, road curvature, and / or slope. Based on the route information, processor 110 may identify whether the route that vehicle 10 is about to enter includes at least one curved road segment.

[0055] The processor 110 may use the camera 140 to acquire an image of the path that the vehicles 10 performing cluster driving are about to enter. For example, the processor 110 may use the forward-facing camera 140 mounted on the lead vehicle 11 to acquire an image of the path that the vehicles 10 performing cluster driving are about to enter. Based on this image, the processor 110 may identify whether the path that the vehicles 10 are about to enter includes at least one curved section. Even if a road does not include a curved section, vehicles traveling on the road may still need to navigate as if on a curved section due to obstacles (or structures) on the road. Therefore, based on the image of the path that the vehicles 10 are about to enter, the processor 110 may identify whether the path that the vehicles 10 are about to enter includes at least one curved section.

[0056] Processor 110 may obtain curvature information about the path that vehicle 10 is about to enter based on at least one of the path information identified in the electronic map and / or the image captured by camera 140. According to an embodiment, processor 110 may obtain high-definition (HD) map data from a server. Processor 110 obtains curvature information about the path that vehicle 10 is about to enter based on the HD map data. According to an embodiment, processor 110 may also obtain curvature information about the path that vehicle 10 is about to enter based on standard definition (SD) map data stored in memory 120. However, this is not limiting.

[0057] According to one embodiment, the processor 110 may identify curvature information about a path that the vehicle 10 is about to enter based on an electronic map. A specific example of identifying curvature information about a path that the vehicle 10 is about to enter based on an electronic map will be described in detail later. Figure 3b is described in .

[0058] refer to Figure 3b Processor 110 may identify the current speed of vehicle 10. Processor 110 may also identify multiple predicted positions of vehicle 10 (e.g., lead vehicle 11) on the upcoming path at specified time intervals. For example, the multiple positions may include a starting position and an ending position of a curved path.

[0059] For example, the plurality of locations may include location 381, location 382, ​​location 383, location 384, and location 385. Location 381, location 382, ​​location 383, location 384, and location 385 may be identified based on the speed of vehicle 10 (e.g., lead vehicle 11). Processor 110 may identify location 381, location 382, ​​location 383, location 384, and location 385 based on the speed of vehicle 10 (e.g., lead vehicle 11) and a specified time interval.

[0060] Processor 110 may identify line segment 389 between location 381, where the curved path begins, and location 385, where the curved path ends. Processor 110 may identify the perpendicular distances from location 382, ​​location 383, and location 384 to line segment 389, respectively. Processor 110 may identify distance 391 from location 382 to line segment 389. Processor 110 may identify distance 392 from location 383 to line segment 389. Processor 110 may identify distance 393 from location 384 to line segment 389.

[0061] The processor 110 may identify the distance 392 having the largest length among the distances 391, 392, and 393. The processor 110 may identify the position 383 corresponding to the distance 392.

[0062] The processor 110 may identify a circumscribed circle 390 based on the position 381, the position 383, and the position 385. The processor 110 may identify a radius 398 of the circumscribed circle 390. The radius 398 of the circumscribed circle 390 may be identified according to the following mathematical formula.

[0063]

Mathematical formula 1

[0064]

[0065] Referring to Mathematical Formula 1, a is the distance 387 between position 381 and position 383. b is the distance 388 between position 383 and position 385. c is the distance 389 between position 381 and position 385. R is the radius 398.

[0066] The processor 110 may identify the curvature and the radius of curvature of the curved path based on the identified radius 398 of the circumscribed circle 390 .

[0067] Reference again Figure 3a In operation 320 , the processor 110 may identify a curved road section having a curvature greater than or equal to a reference curvature on the path. Based on the curvature information, the processor 110 may identify a curved road section having a curvature greater than or equal to a reference curvature on the path that the vehicles 10 performing cluster driving are about to enter.

[0068] For example, on a curved road section with a curvature greater than or equal to a reference curvature, vehicles 10 performing cluster driving may be unable to maintain their speed. Depending on the weight and / or length of each vehicle 10, the speed at which the vehicle 10 can safely navigate the curved road section may vary. Therefore, among curved road sections with a curvature greater than or equal to the reference curvature, the processor 110 may identify such curved road sections and adjust the speed and / or movement path of the vehicles 10 performing cluster driving.

[0069] For example, the reference curvature may be changed according to the vehicle 10. The reference curvature when all the vehicles 10 are cars may be set to be larger than the reference curvature when all the vehicles 10 are trucks.

[0070] In operation 330, the processor 110 may obtain vehicle-related information from each vehicle 10. For example, the vehicle-related information may include the length and / or weight of the vehicle. According to an embodiment, the vehicle-related information may also include vehicle status (e.g., remaining fuel, brake status, battery status, battery charge status, range, tire pressure or engine oil pressure), vehicle model, vehicle size, vehicle overall height, vehicle width, tire size, and / or vehicle body control mode (e.g., four-wheel drive (4WD), front-wheel drive (FWD), rear-wheel drive (RWD)). For example, the vehicle 10 may include a truck with a tractor and a trailer. The weight of the truck may be the sum of the weight of the tractor and the weight of the trailer. The weight of the truck may be the sum of the weight of the tractor and the weight of the trailer. The length of the truck may be the sum of the length of the tractor and the length of the trailer. The length of the truck may be the sum of the length of the tractor and the length of the trailer. The length of the truck may be the length of the tractor and the length of the trailer when connected.

[0071] For example, the processor 110 may utilize the wireless communication device 130 to obtain vehicle-related information from each vehicle 10. For example, the processor 110 may obtain vehicle-related information via at least one of a wireless local area network (e.g., a wireless local area network compliant with the 802.11p standard or the 802.11bd standard), Bluetooth, and / or cellular communication.

[0072] In FIG3 , operation 330 is described as being performed after operation 320 , but is not limited thereto. Operation 330 may also be performed before operation 310 . The processor 110 may acquire vehicle-related information from each vehicle 10 and store the acquired vehicle-related information in the memory 120 .

[0073] In operation 340, the processor 110 may obtain risk-related information for each vehicle 10 when traveling on the curved road section. For example, the processor 110 may use the vehicle-related information obtained from each vehicle 10 and the curvature of the curved road section to determine the risk-related information for each vehicle 10 when traveling on the curved road section.

[0074] For example, the risk level may represent the probability of at least one of a rollover, accident, and / or overturn occurring. Based on vehicle-related information, processor 110 may identify the probability of a rollover, accident, and / or overturn occurring based on the vehicle's speed when traveling on a curved road section. For example, when a truck, including a tractor and trailer, is traveling at the same speed as a car on a curved road section, the truck's risk level may be higher than that of the car. For example, the greater the curvature of a curved road section, the higher the risk level.

[0075] The processor 110 may identify the risk level of the vehicle using at least one of the curvature of the curved road section, the length of the vehicle, and / or the weight of the vehicle. The processor 110 may obtain risk-related information for each of the vehicles 10 performing group driving based on the vehicle-related information obtained from each vehicle 10 and the curvature of the curved road section.

[0076] In operation 350, the processor 110 may determine the speed of the vehicle 10 traveling on the curved road section. For example, the processor 110 may determine the speed of the vehicle 10 traveling on the curved road section based on the risk-related information. According to an embodiment, the processor 110 may not only determine the speed of the vehicle 10 traveling on the curved road section but also identify the corresponding movement path.

[0077] According to one embodiment, the speeds of the vehicles 10 traveling on the curved road section may be set to be the same. For example, the processor 110 may set the speeds of the vehicles 10 traveling on the curved road section to a speed that enables all vehicles 10 to pass through the curved road section at a risk lower than a baseline risk.

[0078] According to one embodiment, the speeds of vehicles 10 traveling on curved sections can also be independently set. Processor 110 can identify the speeds at which each vehicle 10 navigates the curved section while remaining below a baseline risk. For example, processor 110 can identify a first speed at which a lead vehicle 11 navigates the curved section while remaining below a baseline risk. Processor 110 can also identify a second speed at which a following vehicle 12 navigates the curved section while remaining below the baseline risk. The first and second speeds can be different.

[0079] According to one embodiment, the processor 110 may identify the magnitude of the centripetal force of each vehicle 10. The processor 110 may identify the magnitude of the centripetal force of each vehicle 10 based on the following mathematical formula.

[0080]

Mathematical formula 2

[0081]

[0082] Refer to Mathematical Formula 2, where F is the centripetal force. m is the mass of the vehicle. r is the radius (e.g.: Figure 3b The radius in 391). v is the velocity of the vehicle along the tangential direction.

[0083] According to one embodiment, the processor 110 can identify environmental variables such as the mass of each vehicle 10, the road surface conditions (e.g., the road friction coefficient), the tire conditions (e.g., the tire friction coefficient), and the vertical and horizontal gradients of the road. Based on the environmental variables, the processor 110 can identify a centripetal force threshold. Based on the centripetal force threshold, the processor 110 can also identify the maximum speed at which each vehicle 10 can safely traverse a curved path.

[0084] For example, processor 110 may use an AI model (e.g., a regression model) to identify the maximum speed at which each vehicle 10 can safely traverse a curved path. Depending on the embodiment, the AI ​​model may be stored in memory 120 or in a server connected to electronic device 100. The AI ​​model may be trained based on vehicle type, cargo weight, weather, curvature, slope, tire condition, road surface condition, and / or steering failure speed. The information used to train the AI ​​model is exemplary and not limiting.

[0085] Processor 110 may set environmental variables such as the mass of each vehicle 10, road surface conditions (e.g., road friction coefficient), tire conditions (e.g., tire friction coefficient), and vertical and horizontal gradients of the road as input data for the AI ​​model. Based on the output data of the AI ​​model, processor 110 may identify the maximum speed at which each vehicle 10 can safely traverse a curved path.

[0086] For example, the maximum speed at which each vehicle 10 can safely negotiate a curved path may be identified according to the following table.

[0087]

Table 1

[0088]

[0089] As shown in Table 1, the processor 110 can identify the maximum speed at which each vehicle 10 can safely traverse the curved path. Based on the slowest of the identified speeds, the processor 110 can control the vehicles 10 to traverse the curved path at the same speed. However, the present invention is not limited thereto. The processor 110 can also control the vehicles 10 to traverse the curved path at different speeds.

[0090] In operation 360, the processor 110 may transmit the determined speeds to the vehicles 10, respectively. By transmitting the determined speeds to the vehicles, the processor 110 may control the vehicles 10 to pass through the curved road section based on the determined speeds.

[0091] According to one embodiment, the processor 110 may identify the formation of vehicles 10 performing cluster driving before the vehicles 10 enter the curved road section. The processor 110 may store information related to the formation of the vehicles 10 in the memory 120. The processor 110 may control the vehicles 10 to form the stored formation after all vehicles 10 have passed the curved road section.

[0092] Operations 310 to 360 in FIG3 are described as the electronic device 100 being included in the lead vehicle 11, but are not limited thereto. Operations 310 to 360 in FIG3 may also be performed by a server independent of the vehicle 10 (e.g., Figure 1 Server 35) in is executed.

[0093] Figure 4 FIG1 shows a signal flow chart related to the operation of an electronic device and other electronic devices according to an embodiment. In the following embodiments, the operations can be performed in sequence, but they do not necessarily have to be performed in sequence. For example, the order of the operations can be changed, and at least two operations can be performed in parallel. Figure 4 In the embodiment, the operation of the electronic device 100 is performed by the processor 110 of the electronic device 100, and the operations of the other electronic devices 200 (or other electronic devices 201) can be performed by the respective processors 210 of the other electronic devices 200. However, for the sake of convenience, it will be assumed that the operation of the electronic device 100 is performed by the electronic device 100, and the operations of the other electronic devices 200 (or other electronic devices 201) are performed by the other electronic devices 200 (or other electronic devices 201).

[0094] refer to Figure 4 In operation 401, the electronic device 100 may obtain information about a route using an electronic map and an image of the route using the camera 140. For example, the electronic device 100 may obtain information about the route that the vehicles 10 performing group driving are about to enter using an application that provides an electronic map (e.g., a map application or a navigation application). The electronic device 100 may use the camera 140 in front of the lead vehicle 11 to obtain an image of the route that the vehicles 10 performing group driving are about to enter.

[0095] In operation 402, the electronic device 100 may obtain curvature information of a path that the vehicles 10 performing cluster driving are about to enter. For example, the electronic device 100 may obtain the curvature information of the path based on the path information and the image of the path. Operations 401 and 402 may correspond to operation 310 of FIG. 3 .

[0096] In operation 403, the electronic device 100 may identify a curved road section with a curvature greater than or equal to a reference curvature. For example, the electronic device 100 may identify that the vehicles 10 performing cluster driving are about to enter a curved road section with a curvature greater than or equal to the reference curvature. Operation 403 may correspond to operation 320 in FIG. 3 .

[0097] In operation 404 , the electronic device 100 may receive vehicle-related information including vehicle weight and / or vehicle length from the other electronic devices 200 . The other electronic devices 200 may send vehicle-related information including vehicle weight and / or vehicle length to the electronic device 100 .

[0098] For example, the electronic device 100 may receive vehicle-related information contained in the other electronic device 201, including the weight of the vehicle in which the other electronic device 201 is located and / or the length of the vehicle in which the other electronic device 201 is located, from the other electronic device 201. The other electronic device 201 may send vehicle-related information contained in the other electronic device 201 to the electronic device 100, including the weight of the vehicle in which the other electronic device 201 is located and / or the length of the vehicle in which the other electronic device 201 is located.

[0099] Although not shown, the electronic device 100 may identify the weight of the lead vehicle 11 and / or the length of the lead vehicle 11 stored in the memory 120. Operation 404 may correspond to operation 330 of FIG.

[0100] In operation 405, the electronic device 100 may obtain risk-related information for each vehicle 10. For example, the electronic device 100 may obtain risk-related information for each vehicle 10 based on vehicle-related information obtained from each other electronic device 200. Operation 405 may correspond to operation 340 of FIG.

[0101] For example, the electronic device 100 may obtain risk-related information about the vehicle in which the other electronic device 201 is located based on information about the vehicle in which the other electronic device 201 is located obtained from the other electronic device 201. The electronic device 100 may identify the probability of the vehicle in which the other electronic device 201 is located being subjected to a rollover, accident, and / or overturning based on the curvature of the curved road section and / or information about the vehicle in which the other electronic device 201 is located.

[0102] In operation 406, the electronic device 100 may determine the speed and movement path of the vehicle 10 used to travel on the curved road section. For example, the electronic device 100 may determine the speed and movement path of the vehicle in which the other electronic device 201 is located used to travel on the curved road section. Operation 406 may correspond to operation 350 in FIG. 3 .

[0103] In operation 407, the electronic device 100 may transmit the speed and movement path of the vehicle used to travel on the curved road section to each other electronic device 200. For example, the electronic device 100 may transmit the speed and movement path of the vehicle used to travel on the curved road section to the other electronic device 201. The other electronic device 201 may receive the speed and movement path of the vehicle used to travel on the curved road section from the electronic device 100. Operation 407 may correspond to operation 360 in FIG.

[0104] In operation 408, the electronic device 100 may identify whether the vehicle 10 has exited the curved road section. The electronic device 100 may identify whether all vehicles 10 have exited the curved road section based on the location information of the vehicle 10. According to an embodiment, the electronic device 100 may monitor the driving information of the vehicle 10. The electronic device 100 may identify whether all vehicles 10 have exited the curved road section based on the driving information. The electronic device 100 may perform operation 408 until all vehicles 10 have exited the curved road section.

[0105] In operation 409, when the vehicle 10 exits the curved section, the electronic device 100 can control the vehicle 10 to form a specified formation. For example, the electronic device 100 can control the vehicle 10 to form a specified formation based on recognizing that the vehicle 10 has exited the curved section. The specified formation can be the formation of the vehicle 10 before entering the curved section. For example, the specified formation can be a formation set based on the environment after exiting the curved section. For example, other electronic devices 201 can receive a signal for forming a specified formation from the electronic device 100. Based on the received signal, the other electronic device 201 can control the vehicle in which the other electronic device 201 is located to form a specified formation.

[0106] Figure 5 FIG. 1 shows a formation of vehicles before entering a curved road section according to one embodiment.

[0107] refer to Figure 5The vehicles 10 performing cluster driving may include a leading vehicle 11 and a following vehicle 12. The following vehicles 12 may include a vehicle 15, a vehicle 16, and a vehicle 17. For example, the leading vehicle 11 may be a car (e.g., a sedan). The vehicle 15 in the following vehicle 12 may be a truck including a tractor and a trailer. Specific examples of the vehicle 15 will be described in detail below. Figure 10c and Figure 10d The following vehicles 12 include vehicles 16 and 17 that can be sedans.

[0108] exist Figure 5 In the embodiment, for the sake of convenience, an example is shown in which the vehicle 10 includes four vehicles, but the present invention is not limited thereto. The number of the vehicles 10 can be set in various ways as needed. Figure 5 The configuration of the vehicle 10 shown in FIG. 1 is for illustration purposes only and is not intended to be limiting.

[0109] For example, lead vehicle 11 may include electronic device 100. Follower vehicle 12 may include other electronic device 200. Vehicle 15 may include other electronic device 201. Vehicle 16 may include other electronic device 202. Vehicle 17 may include other electronic device 203. Electronic device 100 may control vehicle 10 based on sending signals to other electronic devices 200. For example, electronic device 100 may control vehicle 15 based on sending signals to other electronic device 201. Electronic device 100 may control vehicle 16 based on sending signals to other electronic device 202. Electronic device 100 may control vehicle 17 based on sending signals to other electronic device 203.

[0110] In the following description, for convenience of explanation, the electronic device 100 transmitting a signal to another electronic device 200 to control the operation of the vehicle 10 is described as the electronic device 100 controlling the operation of the vehicle 10 .

[0111] According to one embodiment, the electronic device 100 can control the vehicles 10 to perform cluster driving. The electronic device 100 can form a group of vehicles 10. The electronic device 100 can control the formed group to move along the same path. For example, the electronic device 100 can identify the driving path of the vehicles 10 to the destination. Figure 5 The path shown may represent a portion of the path that vehicle 10 will take to reach its destination. Figure 5 The path shown may be composed of two lanes 40. The two lanes 40 may include lane 41 and lane 42. At time 500, the vehicle 10 may be in a state of traveling in lane 41. For example, Figure 5 The path shown may include a curved segment 510. The curved segment 510 may have a curvature greater than or equal to a specified curvature. Figures 6 to 10bWill be based on Figure 5 The formation shown illustrates an example of the individual operations (eg, speed and / or movement path) of the vehicles 10 under the control of the electronic device 100 .

[0112] Figure 6 An example of an operation of an electronic device for identifying a curved road section according to an embodiment is shown.

[0113] refer to Figure 5 and Figure 6 At time 600, the processor 110 of the electronic device 100 may use an electronic map 610 to identify relevant information about the route that the vehicle 10 performing cluster driving is about to enter. For example, the electronic map 610 may be provided by a navigation application. The processor 110 may identify relevant information about the route that the vehicle 10 is about to enter based on the electronic map 610. Based on the route information, the processor 110 may identify whether the route that the vehicle 10 is about to enter includes at least one curved road segment. For example, the processor 110 may use the electronic device 100 to identify the curved road segment 510 on the route that the vehicle 10 is about to enter.

[0114] The processor 110 may use the camera 140 to acquire an image 620 of the path that the vehicles 10 performing cluster driving are about to enter. The processor 110 may use the camera 140 in front of the lead vehicle 11 to acquire an image 620 of the path that the vehicles 10 performing cluster driving are about to enter. The image 620 may show the curved road section 510. The processor 110 may identify the curved road section 510 based on the image 620.

[0115] The processor 110 can identify whether the curved road section 510 has a curvature greater than or equal to the reference curvature. The processor 110 can obtain vehicle-related information including at least one of vehicle weight or vehicle length from each vehicle 10. The processor 110 can identify the speed and movement path of each vehicle 10 for passing through the curved road section 510 based on the vehicle-related information. Figures 7a to 10b An operation example of the electronic device 100 controlling the vehicle 10 to pass through the curved road section 510 will be described.

[0116] Figure 7a and Figure 7b An operation example of an electronic device for controlling a vehicle to pass through a curved road section according to an embodiment is shown.

[0117] refer to Figure 5 、 Figure 7a and Figure 7b As time passes through time point 500, time point 710, and time point 720, the vehicle 10 may pass through the curved road section 510. The processor 110 of the electronic device 100 may control the vehicle 10 to pass through the curved road section 510.

[0118] According to one embodiment, processor 110 may control vehicles 10 to maintain the same speed and movement path of each vehicle 10 while vehicles 10 are traversing curved road section 510. For example, processor 110 may control vehicles 10 to maintain distances 701, 702, and 703. Distance 701 is the distance between lead vehicle 11 and vehicle 15. Distance 702 is the distance between vehicle 15 and vehicle 16. Distance 703 is the distance between vehicle 16 and vehicle 17. Distances 701, 702, and 703 may remain consistent during the changes to time points 500, 710, and 720. Because the speeds and movement paths of vehicles 10 are controlled to remain the same, distances 701, 702, and 703 may remain the same.

[0119] According to one embodiment, the processor 110 may determine candidate speeds for the vehicles 10 to travel on the curved road segment 510 based on the risk level of each vehicle 10 .

[0120] For example, the determined candidate speeds may differ from one another. Processor 110 may determine a first candidate speed for leading vehicle 11 traveling on curved road section 510. For example, processor 110 may determine the first candidate speed for leading vehicle 11 traveling on curved road section 510 based on at least one of the curvature of curved road section 510, the length of leading vehicle 11, and / or the weight of leading vehicle 11. The risk of leading vehicle 11 traveling on curved road section 510 at the first candidate speed may be lower than the baseline risk.

[0121] The processor 110 may determine a second candidate speed for the vehicle 15 to travel on the curved road section 510. For example, the processor 110 may determine the second candidate speed for the vehicle 15 to travel on the curved road section 510 based on at least one of the curvature of the curved road section 510, the length of the vehicle 15, and / or the weight of the vehicle 15. The risk level of the vehicle 15 traveling the curved road section 510 at the second candidate speed may be lower than the baseline risk level.

[0122] The processor 110 may determine a third candidate speed for the vehicle 16 to travel on the curved road segment 510. For example, the processor 110 may determine the third candidate speed for the vehicle 16 to travel on the curved road segment 510 based on at least one of the curvature of the curved road segment 510, the length of the vehicle 16, and / or the weight of the vehicle 16. The risk of the vehicle 16 traveling the curved road segment 510 at the third candidate speed may be lower than the baseline risk.

[0123] The processor 110 may determine a fourth candidate speed for the vehicle 17 to travel on the curved road section 510. For example, the processor 110 may determine the fourth candidate speed for the vehicle 17 to travel on the curved road section 510 based on at least one of the curvature of the curved road section 510, the length of the vehicle 17, and / or the weight of the vehicle 17. The risk level of the vehicle 17 traveling the curved road section 510 at the fourth candidate speed may be lower than the baseline risk level.

[0124] The processor 110 may identify the lowest candidate speed among the first, second, third, and fourth candidate speeds. The processor 110 may uniformly set the speed of the vehicle 10 to the lowest candidate speed among the first, second, third, and fourth candidate speeds.

[0125] According to an embodiment, while the vehicles 10 pass through the curved road section 510, the processor 110 may not change the movement path of each vehicle 10. While the vehicles 10 pass through the curved road section 510, the processor 110 may keep the vehicles 10 traveling in the lane 41. However, the present invention is not limited thereto.

[0126] Figure 8a and Figure 8b An operation example of an electronic device for controlling a vehicle to pass through a curved road section according to an embodiment is shown.

[0127] refer to Figure 5 、 Figure 8a and Figure 8b As time passes through time point 500, time point 810, and time point 820, the vehicle 10 may pass through the curved road section 510. The processor 110 of the electronic device 100 may control the vehicle 10 to pass through the curved road section 510.

[0128] According to one embodiment, the processor 110 may set the distance between vehicles 10 while the vehicles 10 are passing through the curved road section 510. For example, while the vehicles 10 are passing through the curved road section 510, the processor 110 may change distance 801, distance 802, and distance 803. Distance 801 is the distance between the lead vehicle 11 and vehicle 15. Distance 802 is the distance between vehicle 15 and vehicle 16. Distance 803 is the distance between vehicle 16 and vehicle 17.

[0129] For example, at time point 500 , distance 801 , distance 802 , and distance 803 may be set to be the same. While the vehicle 10 is passing through the curved road section 510 , the processor 110 may set different distances 801 , distance 802 , and distance 803 .

[0130] According to one embodiment, the processor 110 may identify risk-related information of each vehicle 10 . The processor 110 may determine (or identify) the distance 801 , the distance 802 , and the distance 803 based on the risk-related information of each vehicle 10 .

[0131] For example, processor 110 can identify risk-related information for lead vehicle 11 and risk-related information for vehicle 15. Processor 110 can determine distance 801 based on the risk-related information for lead vehicle 11 and risk-related information for vehicle 15. Processor 110 can identify risk-related information for vehicle 15 and risk-related information for vehicle 16. Processor 110 can determine distance 802 based on the risk-related information for vehicle 15 and risk-related information for vehicle 16. Processor 110 can identify risk-related information for vehicle 16 and risk-related information for vehicle 17. Processor 110 can determine distance 803 based on the risk-related information for vehicle 16 and risk-related information for vehicle 17. Processor 110 can send distance 801, distance 802, and distance 803 to vehicle 10.

[0132] According to one embodiment, the processor 110 may identify (or infer) the braking distance of each vehicle 10 within the curved road section 510 based on the vehicle-related information received from each vehicle 10. The processor 110 may determine the distance 801, the distance 802, and the distance 803 based on the braking distance of each vehicle 10 within the curved road section 510. The processor 110 may send the distance 801, the distance 802, and the distance 803 to the vehicle 10.

[0133] At time 810, while vehicle 10 is passing through curved road segment 510, distances 801, 802, and 803 may be set to be different. The risk level of vehicle 15 may be higher than the risk levels of other vehicles 11, 16, and 17. Therefore, distances 801 and 802 associated with vehicle 15 may be longer than distance 803.

[0134] According to one embodiment, the electronic device 100 may pre-change the distance 801, the distance 802, and the distance 803 before the lead vehicle 11 enters the curved road section 510 based on the control of the vehicle 10. According to one embodiment, the electronic device 100 may change the distance 801, the distance 802, and the distance 803 after the vehicle 10 (or the lead vehicle 11) enters the curved road section 510 based on the control of the vehicle 10.

[0135] At time 820, the processor 110 may change the distances 801, 802, and 803 to be the same based on the vehicle 10 passing through the curved road section 510. The processor 110 may change the distances 801, 802, and 803 to be the same by controlling the speed of each vehicle 10.

[0136] Figure 9a and Figure 9b An operation example of an electronic device for controlling a vehicle to pass through a curved road section according to an embodiment is shown.

[0137] refer to Figure 5 、 Figure 9a and Figure 9b As time passes through time point 500, time point 910, and time point 920, the vehicle 10 may pass through the curved road section 510. The processor 110 of the electronic device 100 may control the vehicle 10 to pass through the curved road section 510.

[0138] The processor 110 may determine a speed for the vehicle 10 to travel on a curved road section. The processor 110 may set a reference speed based on the determined speed. For example, the processor 110 may set the median (or average) of the determined speeds as the reference speed. The processor 110 may classify each vehicle 10 into one of a first group and a second group based on the reference speed. Depending on the embodiment, the processor 110 may also identify the reference speed and classify each vehicle 10 into one of a plurality of groups.

[0139] For example, one or more vehicles configured to travel through curved road section 510 at a speed lower than or equal to a reference speed may be included in the first group. One or more vehicles configured to travel through curved road section 510 at a speed higher than the reference speed may be included in the second group. For example, lead vehicle 11 and vehicle 15 may be included in the first group. Vehicles 16 and 17 may be included in the second group.

[0140] The processor 110 may set the movement paths of one or more vehicles included in the first group to the inner lane of the curved road section 510. The processor 110 may set the movement paths of one or more vehicles included in the second group to the outer lane of the curved road section 510. For example, the processor 110 may control the leading vehicle 11 and the vehicle 15 included in the first group to travel in the lane 41, which is the inner lane. The processor 110 may control the vehicles 16 and 17 included in the second group to travel in the lane 42, which is the outer lane.

[0141] According to one embodiment, the electronic device 100 may control the leading vehicle 11 and vehicle 15 to travel in lane 41, which is the inner lane, and control vehicles 16 and 17 to travel in lane 42, which is the outer lane, before the leading vehicle 11 enters the curved road section 510, based on the control of the vehicle 10. According to another embodiment, the electronic device 100 may control the leading vehicle 11 and vehicle 15 to travel in lane 41, which is the inner lane, and control vehicles 16 and vehicle 17 to travel in lane 42, which is the outer lane, based on the control of the vehicle 10, after the vehicle 10 enters the curved road section 510. For example, at time 910, the vehicles 10 may be traveling in a different formation than at time 500.

[0142] At time point 920, the processor 110 may control the vehicles 10 to travel in the formation of time point 500 based on the situation that the vehicles 10 pass through the curved road section 510. For example, the processor 110 may control the vehicles 16 and 17 to follow the vehicle 15.

[0143] Figure 10a and Figure 10b An operation example of an electronic device for controlling a vehicle to pass through a curved road section according to an embodiment is shown.

[0144] refer to Figure 5 、 Figure 10a and Figure 10b As time passes through time point 500, time point 1010, and time point 1020, the vehicle 10 may pass through the curved road section 510. The processor 110 of the electronic device 100 may control the vehicle 10 to pass through the curved road section 510.

[0145] The processor 110 may receive vehicle-related information from each vehicle 10. For example, the processor 110 may receive length-related information for each vehicle 10. The processor 110 may identify one or more vehicles whose length is less than or equal to a specified length. The processor 110 may also identify one or more vehicles whose length exceeds a specified length.

[0146] For example, processor 110 may identify that the lengths of lead vehicle 11, vehicle 16, and vehicle 17 are less than or equal to a specified length. Processor 110 may identify that the length of vehicle 15 exceeds a specified length. Processor 110 may identify that lead vehicle 11 is ahead of vehicle 15. Processor 110 may identify that vehicle 15 is ahead of vehicle 16.

[0147] For example, a vehicle 15 exceeding a specified length may intrude into other lanes designated as non-travel lanes when traveling on a curved road section 510. For example, large vehicles such as large trucks towing trailers and buses have a larger turning radius than ordinary cars, so at least a portion of the vehicle 15 may intrude into other lanes when traveling on a curved road section 510. For example, a vehicle 15 exceeding a specified length may overturn and / or roll over when traveling on a curved road section 510. This situation may result in a serious accident. To prevent serious accidents, the processor 110 may prevent other vehicles from approaching the area 1011 when the vehicle 15 is traveling on the curved road section 510. The processor 110 may prevent other vehicles other than the vehicle 10 from approaching the area 1011 by changing the driving lanes of the lead vehicle 11 and the vehicle 16 from lane 41 to lane 42.

[0148] The processor 110 may set the movement paths of the leading vehicle 11 and the vehicle 16 to the lane 42, which is the outer lane, of the curved road section 510. The processor 110 may set the movement path of the vehicle 15 (or the vehicle 17) to the lane 41, which is the inner lane, of the curved road section 510. For example, the processor 110 may control the vehicle 15 to travel in the lane 41, which is the inner lane. The processor 110 may control the leading vehicle 11 and the vehicle 16 to travel in the lane 42, which is the outer lane.

[0149] The processor 110 may set the distance between the lead vehicle 11 and the vehicle 16 to be greater than the length of the vehicle 15. The processor 110 may use the lead vehicle 11 and the vehicle 16 to prevent other vehicles from approaching the area 1011.

[0150] According to one embodiment, the electronic device 100 may control the leading vehicle 11 and vehicle 16 to travel in lane 42, which is the outer lane, and control vehicles 15 and 17 to travel in lane 41, which is the inner lane, before the leading vehicle 11 enters the curved road section 510, based on the control of the vehicle 10. According to one embodiment, the electronic device 100 may control the leading vehicle 11 and vehicle 16 to travel in lane 42, which is the outer lane, and control vehicles 15 and vehicles 17 to travel in lane 41, which is the inner lane, based on the control of the vehicle 10, after the vehicle 10 enters the curved road section 510. For example, at time 1010, the vehicles 10 may be traveling in a formation different from that at time 500.

[0151] According to one embodiment, processor 110 may identify whether a vehicle other than vehicle 10 is approaching area 1011. In response to identifying the approach of another vehicle, processor 110 may directly and / or indirectly control at least one of the steering wheel, brake system, and / or driving unit of the other vehicle. For example, processor 110 may temporarily control another vehicle approaching area 1011. Furthermore, processor 110 may provide a warning to another vehicle approaching area 1011, informing it that area 1011 is a high-risk area for accidents. For example, processor 110 may send a warning message to another vehicle approaching area 1011.

[0152] At time 1020, the processor 110 may control the vehicles 10 to travel in the formation at time 500 based on the situation of the vehicles 10 passing through the curved road section 510. For example, the processor 110 may control the lead vehicle 11 to travel in front of the vehicle 15. The processor 110 may control the vehicle 16 to follow the vehicle 15. For example, the processor 110 may change the lanes of the lead vehicle 11 and the vehicle 16 from lane 42 to lane 41.

[0153] Figure 10c and Figure 10d An example of a conventional truck is shown. Over the years, the trucking industry has experienced continued growth and expanded its service offerings to address more complex supply chains. These services include last-mile deliveries, drop-trailer programs, and intermodal transportation (where goods are delivered to their destination using two or more different modes of transportation, such as ship and rail, or ship and aircraft) through ports.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] refer to Figure 10c and Figure 10d , vehicle 1015 may be the above Figure 10a and Figure 10b10 is an example of a vehicle 15. The vehicle 1015 includes a tractor or tractor unit 1051 and a semi-trailer 1052. Figure 10c The tractor 1051 and the semi-trailer 1052 are not connected. Figure 10d The state where the tractor 1051 is connected to the semi-trailer 1052 is shown.

[0165] In one embodiment, the semi-trailer 1052 can be selectively connected via a steerable wheel hook 1056 on the tractor 1051. The steerable wheel hook 1056 can be connected to a tow pin 1058 fixed to the semi-trailer 1052 in a known manner. The vehicle 1015 including the tractor 1051 and the semi-trailer 1052 can be referred to as a truck. The vehicle 1015 can also include only the tractor 1051. Figure 10c and Figure 10d The semi-trailer 1052 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 10c and Figure 10d The tractor 1051 is shown in the form of a "flat-top 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 "flat-top truck".

[0166] In one embodiment, the semi-trailer 1052 may include a tow pin 1058 connected to the steering wheel hook 1056 of the tractor 1051, and support legs 1059 for supporting the semi-trailer 1052 on the ground when the semi-trailer 1052 is not connected to the tractor 1051. The tow pin 1058 and the support legs 1059 may be installed (configured) on the bottom of the semi-trailer 1052.

[0167] In one embodiment, to support travel on curved roads, the semi-trailer 1052 can be rotatably connected to the tractor 1051. For example, the tractor 1051 and the semi-trailer 1052 can be rotatably connected via a coupling device including a steering wheel hook 1056 and a towing pin 1058. However, the connection mechanism between the tractor 1051 and the semi-trailer 1052 is not limited thereto.

[0168] Figure 11 An example block diagram of an autonomous driving system for a vehicle according to one embodiment is shown.

[0169] according to Figure 11, the vehicle automatic driving system 1100 can be a deep learning network including a sensor 1103, an image preprocessor 1105, a deep learning network 1107, an artificial intelligence (AI) processor 1109, a vehicle control module 1111, a network interface 1113 and a communication unit 1115. In various embodiments, the various components can be connected through different interfaces. For example, the sensor data sensed and output by the sensor 1103 can be fed to the image preprocessor 1105. The sensor data processed by the image preprocessor 1105 can be fed to the deep learning network 1107 run by the AI ​​processor 1109. The output of the deep learning network 1107 run by the AI ​​processor 1109 can be fed to the vehicle control module 1111. The intermediate results of the deep learning network 1107 running on the AI ​​processor 1109 can be fed to the AI ​​processor 1109. In various embodiments, the network interface 1113 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 1100 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 1113 can be used to transmit sensor data obtained by the sensor 1103 to an external server. In some embodiments, the autonomous driving control system 1100 may include additional or fewer components as appropriate. For example, in some embodiments, the image preprocessor 1105 may be an optional component. For another example, a post-processing module (not shown) may be included in the autonomous driving control system 1100 to perform post-processing on the output of the deep learning network 1107 before providing the output to the vehicle control module 1111.

[0170] In some embodiments, sensor 1103 may include more than one sensor. In various embodiments, sensor 1103 may be installed at different locations on the vehicle. Sensor 1103 may face one or more different directions. For example, sensor 1103 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 1103 may be an image sensor, such as a high dynamic range camera. In some embodiments, sensor 1103 may include non-visual sensors. In some embodiments, sensor 1103 may include radar, laser radar (LiDAR), and / or ultrasonic sensors in addition to image sensors. In some embodiments, sensor 1103 is not mounted on the vehicle having vehicle control module 1111. For example, sensor 1103 may be part of a deep learning system for capturing sensor data and may be installed in the environment or on a road, and / or on surrounding vehicles.

[0171] In some embodiments, the image pre-processor 1105 can be used to pre-process the sensor data of the sensor 1103. For example, the image pre-processor 1105 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 1105 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 1105 can be a tone-mapper processor for processing high dynamic range data. In some embodiments, the image pre-processor 1105 can be a component of the AI ​​processor 1109.

[0172] In some embodiments, deep learning network 1107 may be a deep learning network for implementing control commands for controlling an autonomous vehicle. For example, deep learning network 1107 may be an artificial neural network, such as a convolutional neural network (CNN), trained using sensor data, and the output of deep learning network 1107 is provided to vehicle control module 1111.

[0173] In some embodiments, the artificial intelligence (AI) processor 1109 may be a hardware processor for running the deep learning network 1107. In some embodiments, the AI ​​processor 1109 may be a specialized AI processor for performing inference on sensor data using a convolutional neural network (CNN). In some embodiments, the AI ​​processor 1109 may be optimized for the bit depth of the sensor data. In some embodiments, the AI ​​processor 1109 may be optimized for deep learning operations (such as operations in a neural network that include convolution, inner product, vector and / or matrix operations). In some embodiments, the AI ​​processor 1109 may be implemented by multiple graphics processing units (GPUs) that can effectively perform parallel processing.

[0174] In various embodiments, the AI ​​processor 1109 can be coupled to a memory storing instructions via an input / output interface. When executed by the AI ​​processor 1109, the instructions can perform deep learning analysis on the sensor data from the sensor 1103 and generate machine learning results for enabling the vehicle to operate at least partially autonomously. In certain embodiments, the vehicle control module 1111 can process the vehicle control instructions output by the artificial intelligence (AI) processor 1109 and translate the output of the AI ​​processor 1109 into instructions for controlling various modules of the vehicle to control the various modules of the vehicle. In certain embodiments, the vehicle control module 1111 can be used to control the vehicle to achieve autonomous driving. In certain embodiments, the vehicle control module 1111 can adjust the steering and / or speed of the vehicle. For example, the vehicle control module 1111 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 1111 can generate control signals for controlling vehicle lighting, such as brake lights, turn signals, and headlights. In some embodiments, the vehicle control module 1111 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.

[0175] In some embodiments, the vehicle control module 1111 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 1111 can be used to adjust vehicle sensors, such as sensor 1103. For example, the vehicle control module 1111 can modify the orientation of sensor 1103, change the output resolution and / or format type of sensor 1103, increase or decrease the capture rate, adjust the dynamic range, and adjust the focal length of the camera. In addition, the vehicle control module 1111 can turn the operation of sensors on or off individually or collectively.

[0176] In certain embodiments, the vehicle control module 1111 can be used to modify parameters of the image preprocessor 1105, 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 1111 can be used to control autonomous driving functions and / or driver assistance functions of the vehicle.

[0177] In certain embodiments, the network interface 1113 may serve as an internal interface between modules of the autonomous driving control system 1100 and the communication unit 1115. Specifically, the network interface 1113 may serve as a communication interface for receiving and / or transmitting data, including voice data. In various embodiments, the network interface 1113 may connect to an external server via the communication unit 1115 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 to update the vehicle's autonomous driving system software.

[0178] In various embodiments, the communication unit 1115 may include a variety of wireless interfaces such as cellular or WiFi. For example, the network interface 1113 may be connected to an external server via the communication unit 1115 to receive updates on operating parameters and / or instructions for the sensor 1103, the image preprocessor 1105, the deep learning network 1107, the AI ​​processor 1109, and the vehicle control module 1111. For example, the machine learning model of the deep learning network 1107 may be updated via the communication unit 1115. In another example, the communication unit 1115 may be used to update operating parameters (such as image processing parameters) of the image preprocessor 1105 and / or the firmware of the sensor 1103.

[0179] In other embodiments, the communication unit 1115 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 1115 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 1115 can also be used to update or obtain an estimated time of arrival and / or destination location.

[0180] According to one embodiment, Figure 11The illustrated autonomous driving system 1100 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 1109 of the autonomous driving system 1100 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.

[0181] Figure 12 and Figure 13 An example block diagram of an autonomous driving mobile body according to one embodiment is shown. Figure 14 An example of a gateway is shown in relation to a user device in various embodiments.

[0182] Reference Figure 12 According to this embodiment, the autonomous driving mobile body 1200 may include a control device 1300, perception modules 1204a, 1204b, 1204c, 1204d, an engine 1206 and a user interface 1208.

[0183] The autonomous vehicle 1200 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 1208.

[0184] When the moving object 1200 operates in the autonomous driving mode, the autonomous driving moving object 1200 may operate under the control of the control device 1300 .

[0185] In this embodiment, the control device 1300 may include a controller 1320 having a memory 1322 and a processor 1324 , a sensor 1310 , a communication device 1330 , and an object detection device 1340 .

[0186] The object detection device 1340 may perform all or part of the functions of the distance measurement device.

[0187] That is, in this embodiment, the object detection device 1340 is a device for detecting an object located outside the moving body 1200. The object detection device 1340 can detect an object located outside the moving body 1200 and generate object information based on the detection result.

[0188] 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.

[0189] Objects may include lane markings, other vehicles, pedestrians, traffic signals, light, roads, structures, speed bumps, terrain features, animals, and other objects external to the mobile object 1200. 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, streetlights, or sunlight.

[0190] 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.

[0191] The object detection device 1340 may include a camera module. The controller 1320 may extract object information from an external image captured by the camera module and process the information related thereto.

[0192] Furthermore, object detection device 1340 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.

[0193] On the other hand, according to one embodiment of the present invention, the distance measuring device can calculate the distance between the autonomous driving mobile body 1200 and the object, and in combination with the control device 1300 of the autonomous driving mobile body 1200, control the movement of the mobile body based on the calculated distance.

[0194] For example, when the distance between the autonomous vehicle 1200 and an object is likely to conflict, the autonomous vehicle 1200 can control the brakes to reduce speed or stop. Another example is when the object is moving, the autonomous vehicle 1200 can control its speed to maintain a predetermined distance from the object.

[0195] According to one embodiment of the present invention, such a distance measurement device may be configured as a module in the control device 1300 of the autonomous driving mobile body 1200. In other words, the memory 1322 and processor 1324 of the control device 1300 may implement the anti-collision method of the present invention in software.

[0196] In addition, the sensor 1310 can be connected to the perception modules 1204a, 1204b, 1204c, and 1204d to obtain various perception information of the internal / external environment of the mobile object. The sensor 1310 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.

[0197] Therefore, the sensor 1310 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.

[0198] In addition, sensor 1310 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 temperature sensor (ATS), a water temperature sensor (WTS), a throttle position sensor (TPS), a TDC sensor, and a crankshaft angle sensor (CAS).

[0199] As described above, the sensor 1310 may generate mobile object state information based on the sensing data.

[0200] The wireless communication device 1330 is configured to implement wireless communication between the autonomous driving mobile bodies 1200. For example, the autonomous driving mobile body 1200 can communicate with a user's mobile phone, other wireless communication devices 1330, other mobile bodies, a central device (such as a traffic control device), a server, etc. The wireless communication device 1330 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.

[0201] In addition, according to this embodiment, the autonomous driving mobile body 1200 can also achieve communication between mobile bodies through the wireless communication device 1330. That is, the wireless communication device 1330 can communicate with other mobile bodies and other vehicles on the road through vehicle-to-vehicle (V2V) communication. The autonomous driving mobile body 1200 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 1330 can achieve V2V communication through dedicated short-range communication (DSRC) equipment or C-V2V (Cellular-V2V) equipment. In addition to inter-vehicle communication, communication between the vehicle 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 1330.

[0202] In addition, the wireless communication device 1330 can also obtain information from various mobile bodies (Mobility) such as infrastructure located on the road (such as traffic lights, closed-circuit television (Closed-Circuit Television), RSU, eNode B, etc.) or other autonomous driving (Autonomous Driving) / non-autonomous driving (Non-Autonomous Driving) vehicles through a non-terrestrial network (Non-Terrestrial Network), and use it as the information required for the autonomous driving mobile body 1200 to perform autonomous driving.

[0203] For example, the wireless communication device 1330 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 1200.

[0204] For example, the wireless communication device 1330 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.

[0205] In this embodiment, the controller 1320 can consider various information such as the location, current time, available power, etc. of the autonomous driving mobile body 1200, select a platform that can appropriately perform NTN communication, and control the wireless communication device 1330 to perform wireless communication with the selected platform.

[0206] In this embodiment, controller 1320, which controls the overall operation of various units within mobile object 1200, 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 1320 configured at manufacturing time. This type of controller 1320 may also be referred to as an ECU (Electronic Control Unit).

[0207] The controller 1320 can collect various data from connected sensors 1310, object detection device 1340, communication device 1330, etc., and based on the collected data, transmit control signals to other components of the mobile body, including the sensors 1310, engine 1206, user interface 1208, communication device 1330, and object detection device 1340. In addition, although not specifically described, control signals can also be transmitted to the acceleration device, braking system, steering device, or navigation device related to the driving of the mobile body.

[0208] In this embodiment, the controller 1320 can control the engine 1206. For example, when the autonomous vehicle 1200 detects a speed limit on the road, the controller 1320 can control the engine 1206 to ensure that the driving speed does not exceed the speed limit, or accelerate the driving speed of the autonomous vehicle 1200 within a range that does not exceed the speed limit.

[0209] Furthermore, when the autonomous vehicle 1200 approaches or deviates from a lane line during its travel, the controller 1320 can determine whether such approach or deviation constitutes a normal driving situation or another driving situation, and control the engine 1206 based on the determination to adjust the vehicle's travel. Specifically, the autonomous vehicle 1200 can detect lane lines formed on both sides of the lane in which the vehicle is traveling. In this case, the controller 1320 can determine whether the autonomous vehicle 1200 is approaching or deviating from a lane line. If it is determined that the autonomous vehicle 1200 is approaching or deviating from a lane line, the controller 1320 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 1320 determines that the autonomous vehicle 1200 is approaching or deviating from a lane line when a lane change is not necessary, the controller 1320 can control the autonomous vehicle 1200 to travel normally in the relevant lane without deviating from the lane line.

[0210] When there are other moving objects or obstacles ahead of the moving object, the engine 1206 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 1320 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.

[0211] In addition to generating its own control signals, the controller 1320 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.

[0212] Furthermore, if the position or viewing angle of the camera module 1350 changes, the controller 1320 may have difficulty accurately identifying the moving object or lane markings as in the present embodiment. To prevent this, the controller 1320 may also generate a control signal to calibrate the camera module 1350. Therefore, in this embodiment, the controller 1320 issues a calibration control signal to the camera module 1350. This ensures that the camera module 1350 maintains its normal installation position, orientation, and viewing angle, even if the installation position of the camera module 1350 changes due to vibration or impact generated by the movement of the autonomous driving moving object 1200. The controller 1320 may generate a control signal to calibrate the camera module 1350 when a change exceeds a threshold between the pre-stored initial installation position, orientation, and viewing angle of the camera module 1350 and the initial installation position, orientation, and viewing angle of the camera module 1350 measured during driving of the autonomous driving moving object 1200.

[0213] In this embodiment, the controller 1320 may include a memory 1322 and a processor 1324. The processor 1324 may execute software stored in the memory 1322 according to control signals from the controller 1320. Specifically, the controller 1320 stores data and commands required for executing the lane detection method described in the present invention in the memory 1322. These commands may be executed by the processor 1324 to implement one or more methods disclosed herein.

[0214] In this case, the memory 1322 may be stored on a recording medium executable by the non-volatile processor 1324. The memory 1322 may store software and data via appropriate internal or external devices. The memory 1322 may be composed of RAM (random access memory), ROM (read only memory), a hard disk, or a memory device connected to a dongle.

[0215] The memory 1322 can store at least an operating system (OS), user applications, and executable commands. The memory 1322 can also store application data and array data structures.

[0216] Processor 1324 may be a microprocessor or suitable electronic processor, and may be a controller, microcontroller, or state machine.

[0217] The processor 1324 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.

[0218] On the other hand, the autonomous mobile object 1200 may also include a user interface 1208 for receiving user input to the control device 1300. The user interface 1208 allows the user to input information through an appropriate interactive method. For example, this may be implemented through a touch screen, keyboard, operation buttons, etc. The user interface 1208 transmits the input or command to the controller 1320, which executes the control action of the mobile object based on the response to the input or command.

[0219] In addition, the user interface 1208 can also communicate with devices outside the autonomous driving mobile body 1200 through the wireless communication device 1330. For example, the user interface 1208 can be linked with a mobile phone, tablet computer or other computer device.

[0220] Furthermore, while the autonomously driven vehicle 1200 described in this embodiment includes an engine 1206, 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 1320 includes a propulsion mechanism specific to the propulsion system of the autonomously driven vehicle 1200 and may provide corresponding control signals to the components of each propulsion mechanism.

[0221] Below, refer to Figure 13 , further describing in detail the detailed structure of the control device 1300 according to this embodiment.

[0222] The control device 1300 includes a processor 1324. The processor 1324 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 1324 can also be used as a combination of multiple processors.

[0223] The control device 1300 further includes a memory 1322. The memory 1322 may be any electronic component capable of storing electronic information. In addition to being a single memory, the memory 1322 may also include a combination of multiple memories 1322.

[0224] The data and command 1322a required for the distance measurement device according to the present invention to execute the distance measurement method may be stored in the memory 1322. When the processor 1324 executes the command 1322a, all or part of the command 1322a and the data 1322b required to execute the command may be loaded onto the processor 1324 (1324a, 1324b).

[0225] The control device 1300 may include a transmitter 1330a, a receiver 1330b, or a transceiver 1330c for allowing signal transmission and reception. One or more antennas 1332a, 1332b may be electrically connected to the transmitter 1330a, the receiver 1330b, or each transceiver 1330c, and may further include an antenna.

[0226] The control device 1300 may further include a digital signal processor (DSP) 1370. The DSP 1370 allows the mobile device to quickly process digital signals.

[0227] The control device 1300 may further include a communication interface 1380. The communication interface 1380 may include one or more ports and / or communication modules for connecting other devices to the control device 1300. The communication interface 1380 allows a user to interact with the control device 1300.

[0228] The various components of the control device 1300 can be connected via one or more buses 1390 , and the bus 1390 can include a power bus, a control signal bus, a status signal bus, a data bus, etc. Under the control of the processor 1324 , the components can communicate information with each other via the bus 1390 and perform predetermined functions.

[0229] On the other hand, in various embodiments, the control device 1300 can be associated with a gateway to communicate with the secure cloud. Figure 14 , the control device 1300 may be associated with a gateway 1405 for providing information acquired from at least one of the components (1401 to 1404) of the vehicle 1400 to a secure cloud 1406. For example, the gateway 1405 may be included in the control device 1300. In another example, the gateway 1405 may be configured as an independent device within the vehicle 1400, separate from the control device 1300. The gateway 1405 can connect the software management cloud 1409, the secure cloud 1406, and the internal network of the vehicle 1400 protected by the in-vehicle security software 1410, which have different networks, to achieve communication.

[0230] For example, component 1401 may be a sensor. For example, the sensor may be used to obtain information about at least one of the state of vehicle 1400 or the state around vehicle 1400. For example, component 1401 may include sensor 1310.

[0231] For example, component 1402 may be an ECU (electronic control unit), which may be used for engine control, transmission control, airbag control, or tire pressure management.

[0232] For example, component 1403 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 1400, a visual element indicating the remaining fuel level, a visual element indicating the gear status, and a visual element indicating information obtained through component 1401.

[0233] For example, component 1404 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 1400 to the driver, the cloud (e.g., the safety cloud 1406) 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-TerrestrialNetwork) technology). For example, the telematics device may support autonomous driving of the vehicle 1400.

[0234] For example, gateway 1405 can be used to connect the network within vehicle 1400 with a software management cloud 1409 and a security cloud 1406, which are external networks. For example, software management cloud 1409 can be used to update or manage at least one software required for driving and managing vehicle 1400. For example, software management cloud 1409 can be linked with in-car security software 1410 installed within the vehicle. For example, in-car security software 1410 can be used to provide security functions within vehicle 1400. For example, in-car security software 1410 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 1410 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).

[0235] In various embodiments, the gateway 1405 can transmit data encrypted by the in-vehicle security software 1410 using the encryption key to the software management cloud 1409 and / or the security cloud 1406. The software management cloud 1409 and / or the security cloud 1406 decrypt the data encrypted by the in-vehicle security software 1410 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 1409 and / or the security cloud 1406 can identify the data sender (e.g., the vehicle or user) based on the data decrypted using the decryption key.

[0236] For example, gateway 1405 is configured to support in-vehicle security software 1410 and may be associated with control device 1300. For example, gateway 1405 may be associated with control device 1300 to support a connection between control device 1300 and client device 1407 connected to secure cloud 1406. According to another example, gateway 1405 may be associated with control device 1300 to support a connection between control device 1300 and a third-party cloud 1408 connected to secure cloud 1406. However, the present invention is not limited thereto.

[0237] In various embodiments, gateway 1405 can be used to connect vehicle 1400 with a software management cloud 1409 for managing the operating software of vehicle 1400. For example, software management cloud 1409 monitors whether the operating software of vehicle 1400 needs to be updated and, upon detecting that the operating software of vehicle 1400 needs to be updated, provides data for updating the operating software of vehicle 1400 via gateway 1405. In another example, software management cloud 1409 receives a user request for updating the operating software of vehicle 1400 from vehicle 1400 via gateway 1405 and, based on the request, provides data for updating the operating software of vehicle 1400. However, the present invention is not limited to this.

[0238] Figure 15 is a diagram for explaining an operation of an electronic device for training a neural network based on a training data set according to one embodiment.

[0239] Reference Figure 15 The described operations can be performed by the above-mentioned electronic devices (e.g., Figure 2 Executed by the electronic device 100).

[0240] refer to Figure 15In operation 1502, 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.

[0241] 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 1502 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.

[0242] Reference Figure 15 In operation 1504, 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 16 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.

[0243] In one embodiment, the training of operation 1504 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 16 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.

[0244] refer to Figure 15 In operation 1506, according to one embodiment, the electronic device may identify whether the neural network trained in operation 1504 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.

[0245] If the neural network does not output valid output data (1506-No), the electronic device may repeatedly perform training of the neural network based on operation 1504. The embodiment is not limited thereto, and the electronic device may repeatedly perform operations 1502 and 1504.

[0246] When valid output data is obtained from the neural network (1506-Yes), the electronic device according to one embodiment may use the trained neural network based on operation 1508. 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.

[0247] Figure 16 is a block diagram of an electronic device according to one embodiment.

[0248] Figure 16 The electronic device 100 may include the aforementioned electronic devices.

[0249] For example, refer to Figure 15 The described operation can be performed by Figure 16 The electronic device 100 and / or Figure 16 Executed by processor 1610.

[0250] Reference Figure 16, the processor 1610 of the electronic device 100 can perform computations related to the neural network 1630 stored in the memory 1620. The processor 1610 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 a chip such as a CPU in the form of a system on a chip (SoC). The NPU integrated in the CPU can be called a neural core and / or an artificial intelligence (AI) accelerator.

[0251] Reference Figure 16 , the processor 1610 may identify a neural network 1630 stored in the memory 1620. The neural network 1630 may include a combination of an input layer 1632, one or more hidden layers 1634 (or intermediate layers), and an output layer 1636. Each of the above layers (e.g., the input layer 1632, one or more hidden layers 1634, and the output layer 1636) may include multiple nodes. The number of hidden layers 1634 may vary depending on the embodiment, and a neural network 1630 including multiple hidden layers 1634 may be referred to as a deep neural network. The operation of training the deep neural network may be referred to as deep learning.

[0252] In one embodiment, if neural network 1630 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 1630 in memory 1620 may include weights assigned to connections between the second node and the first node. In neural network 1630 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.

[0253] In one embodiment, if neural network 1630 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 1620 for neural network 1630 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.

[0254] According to one embodiment, the processor 1610 of the electronic device 100 may train the neural network 1630 using the training data set 1640 stored in the memory 1620. Based on the training data set 1640, the processor 1610 may execute a reference Figure 15 The described operations thereby adjust one or more parameters stored in memory 1620 for neural network 1630.

[0255] According to one embodiment, the processor 1610 of the electronic device 100 can perform object detection, object recognition, and / or object classification using a neural network 1630 trained based on a training data set 1640. The processor 1610 can input an image (or video) acquired through the camera 1650 into the input layer 1632 of the neural network 1630. Based on the input layer 1632 into which the image is input, the processor 1610 can sequentially acquire the values ​​of the nodes of each layer included in the neural network 1630, thereby acquiring a set of values ​​(e.g., output data) of the nodes of the output layer 1636. The output data can be used as a result of reasoning about the information contained in the image using the neural network 1630. The embodiment is not limited thereto, and the processor 1610 can also acquire an image (or video) from an external electronic device connected to the electronic device 100 via the communication circuit 1660 and input it into the neural network 1630.

[0256] In one embodiment, the neural network 1630 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 1630 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 1630 to identify at least one category that matches the object from a plurality of specified categories.

[0257] According to one embodiment, an electronic device for platooning vehicles may include: a camera; a memory for storing instructions; and a processor. When executed by the processor, the instructions may enable the electronic device to obtain curvature information about a path that the vehicles performing platooning are about to enter. When executed by the processor, the instructions may enable the electronic device to identify, based on the curvature information, a curved section on the path whose curvature is greater than or equal to a reference curvature. When executed by the processor, the instructions may enable the electronic device to obtain vehicle-related information, including at least one of vehicle weight or vehicle length, from each vehicle. When executed by the processor, the instructions may enable the electronic device to utilize the vehicle-related information obtained from each vehicle and the curvature of the curved section to obtain risk-related information for each vehicle when traversing the curved section. When executed by the processor, the instructions may enable the electronic device to determine, based on the risk-related information, a speed for each vehicle to travel on the curved section. When executed by the processor, the instructions may enable the electronic device to transmit the determined speed to each vehicle.

[0258] According to one embodiment, when the processor executes the instructions, the electronic device may determine a movement path for the vehicle to travel the curved road section based on the risk-related information. When the processor executes the instructions, the electronic device may transmit the determined speed and the movement path to the vehicle, respectively.

[0259] According to one embodiment, when the processor executes the instructions, the electronic device may be enabled to: identify candidate speeds associated with the vehicles based on the vehicle-related information obtained from each vehicle. When the processor executes the instructions, the electronic device may be enabled to: determine the lowest candidate speed among the candidate speeds as the speed for the vehicle to travel on the curved road section.

[0260] According to one embodiment, the vehicles may include a first vehicle and a second vehicle. When executed by the processor, the instructions may enable the electronic device to: determine the distance between the first vehicle and the second vehicle based on risk-related information of the first vehicle and the risk-related information of the second vehicle. When executed by the processor, the instructions may enable the electronic device to: send the distance to the first vehicle and the second vehicle.

[0261] According to one embodiment, when the processor executes the instructions, the electronic device may be enabled to: set a reference speed based on the determined speed; when the processor executes the instructions, the electronic device may be enabled to: classify each of the vehicles into one of a first group and a second group based on the reference speed; and when the processor executes the instructions, the electronic device may be enabled to: set the movement paths of the vehicles in the first group to the inner lane of the curved road section and set the movement paths of the vehicles in the second group to the outer lane of the curved road section.

[0262] According to one embodiment, the vehicle may include: a first vehicle; a second vehicle following the first vehicle; and a third vehicle following the second vehicle. When the instruction is executed by the processor, the electronic device may be enabled to: set the movement paths of the first vehicle and the third vehicle, whose lengths are less than or equal to a specified length, to the outer lanes of the curved section. When the instruction is executed by the processor, the electronic device may be enabled to: set the movement path of the second vehicle, whose length exceeds the specified length, to the inner lane of the curved section. The distance between the first vehicle and the third vehicle may be set to be greater than the length of the second vehicle.

[0263] According to one embodiment, when the processor executes the instructions, the electronic device may be enabled to: identify a formation of the vehicles before the vehicles enter the curved road section; store information about the formation of the vehicles in the memory; and control the vehicles to form the formation after all the vehicles exit the curved road section.

[0264] According to one embodiment, when the processor executes the instructions, the electronic device may be enabled to: identify information about the path that the vehicles performing group driving are about to enter based on an electronic map. When the processor executes the instructions, the electronic device may be enabled to: capture an image of the path using the camera. When the processor executes the instructions, the electronic device may be enabled to: obtain curvature information of the path based on the path information and the image.

[0265] According to one embodiment, the vehicle may include a truck including a tractor and a trailer. The weight of the truck may be determined as the sum of the weight of the tractor and the weight of the trailer. The length of the truck may be determined as the sum of the length of the tractor and the length of the trailer.

[0266] According to one embodiment, the electronic device may be included in a leading vehicle among the vehicles.

[0267] According to one embodiment, a method for an electronic device for cluster driving of vehicles may include the following operations: obtaining curvature information of a path that the vehicles performing cluster driving are about to enter. The method may include the following operations: identifying a curved section on the path whose curvature is greater than or equal to a reference curvature based on the curvature information. The method may include the following operations: obtaining vehicle-related information including at least one of the weight of the vehicle or the length of the vehicle from each of the vehicles. The method may include the following operations: obtaining risk-related information of each of the vehicles when driving on the curved section using the vehicle-related information obtained from each of the vehicles and the curvature of the curved section. The method may include the following operations: determining the speed of the vehicle for driving on the curved section based on the risk-related information. The method may include the following operations: sending the determined speed to each of the vehicles.

[0268] According to one embodiment, the method may include the following operations: determining a movement path for the vehicle to travel on the curved road section based on the risk-related information. The method may include the following operations: sending the determined speed and the movement path to the vehicle respectively.

[0269] According to one embodiment, the method may include the following operations: identifying candidate speeds associated with the vehicles based on vehicle-related information obtained from each of the vehicles. The method may include the following operations: determining the lowest candidate speed among the candidate speeds as the speed for the vehicle to travel on the curved road section.

[0270] According to one embodiment, the vehicles may include a first vehicle and a second vehicle. The method may include determining a distance between the first vehicle and the second vehicle based on risk-related information of the first vehicle and risk-related information of the second vehicle. The method may include transmitting the distance to the first vehicle and the second vehicle.

[0271] According to one embodiment, the method may include the following operations: setting a reference speed based on the determined speed. The method may include the following operations: classifying each of the vehicles into one of a first group and a second group based on the reference speed. The method may include the following operations: setting the movement paths of the vehicles in the first group to the inner lane of the curved road section, and setting the movement paths of the vehicles in the second group to the outer lane of the curved road section.

[0272] According to one embodiment, the vehicles may include: a first vehicle; a second vehicle following the first vehicle; and a third vehicle following the second vehicle. The method may include the following operations: setting the movement paths of the first vehicle and the third vehicle, whose lengths are less than or equal to a specified length, as outer lanes of the curved road section. The method may include the following operations: setting the movement path of the second vehicle, whose length exceeds the specified length, as an inner lane of the curved road section. The distance between the first vehicle and the third vehicle may be set to be greater than the length of the second vehicle.

[0273] According to one embodiment, the method may include the following operations: identifying a formation of the vehicles before the vehicles enter the curved road section, storing information about the formation of the vehicles in the memory, and controlling the vehicles to form the formation upon all the vehicles exiting the curved road section.

[0274] According to one embodiment, the method may include the following operations: identifying information about the path that the vehicles performing group driving are about to enter based on an electronic map. The method may include the following operations: acquiring an image of the path using the camera. The method may include the following operations: acquiring curvature information of the path based on the path information and the image.

[0275] According to one embodiment, the vehicle may include a truck including a tractor and a trailer. The weight of the truck may be determined as the sum of the weight of the tractor and the weight of the trailer. The length of the truck may be determined as the sum of the length of the tractor and the length of the trailer.

[0276] According to one embodiment, a non-transitory computer-readable storage medium may store one or more programs. The one or more programs may include instructions that, when executed by a processor of an electronic device, are capable of obtaining curvature information of a path that vehicles performing cluster driving are about to enter. The one or more programs may include instructions that, when executed by the processor of the electronic device, are capable of identifying, based on the curvature information, curved sections on the path whose curvature is greater than or equal to a reference curvature. The one or more programs may include instructions that, when executed by the processor of the electronic device, are capable of obtaining vehicle-related information, including at least one of vehicle weight or vehicle length, from each of the vehicles. The one or more programs may include instructions that, when executed by the processor of the electronic device, are capable of using the vehicle-related information obtained from each of the vehicles and the curvature of the curved section to obtain risk-related information for each of the vehicles when traveling on the curved section. The one or more programs may include instructions that, when executed by the processor of the electronic device, are capable of determining, based on the risk-related information, a speed for the vehicle to travel on the curved section. The one or more programs may include instructions that, when executed by the processor of the electronic device, are capable of transmitting the determined speed to each of the vehicles.

[0277] 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.

[0278] In the specific embodiments of the present disclosure described above, the constituent elements included in the disclosure are expressed in singular or plural form according to the description of the specific embodiment. However, the singular or plural expression is selected according to the specific circumstances for the convenience of explanation, and the present disclosure is not limited to singular or plural constituent elements. Even constituent elements expressed in plural form can be composed of a single element, and constituent elements expressed in singular form can also be composed of multiple elements.

[0279] According to an embodiment, one or more constituent elements or operations in the above-mentioned constituent elements can be omitted, or one or more other constituent elements or operations can be added. Alternatively or additionally, multiple constituent elements (for example, modules or programs) can be integrated into one constituent element. In this case, the constituent element after integration can perform these functions in a manner that the corresponding constituent elements in the multiple constituent elements before integration perform the same or similar functions. According to an embodiment, the operations performed by modules, programs or other constituent elements can be performed sequentially, in parallel, repeatedly or heuristically, or one or more of the above-mentioned operations can be performed in different orders, omitted, or one or more other operations can be added.

[0280] On the other hand, although specific embodiments have been described in the detailed description of the present disclosure, it is apparent that various modifications can be made without departing from the scope of the present disclosure.

Claims

1. An electronic device for cluster driving of vehicles, comprising: Camera; a memory for storing instructions; as well as processor, When the instructions are executed by the processor, the electronic device is enabled to: Obtaining curvature information of a path that the vehicles performing cluster driving are about to enter; Based on the curvature information, identifying a curved section on the path having a curvature greater than or equal to a reference curvature; acquiring vehicle-related information including at least one of a weight of the vehicle or a length of the vehicle from each of the vehicles; Using the vehicle-related information obtained from each vehicle and the curvature of the curved road section, obtaining risk-related information for each vehicle when traveling on the curved road section; determining a speed for the vehicle to travel on the curved road section based on the risk-related information; as well as The determined speed is respectively transmitted to the vehicles.

2. The electronic device according to claim 1, wherein When the instructions are executed by the processor, the electronic device is enabled to: determining a movement path for the vehicle to travel on the curved road section based on the risk related information; as well as The determined speed and the moving path are respectively sent to the vehicles.

3. The electronic device according to claim 1, wherein: When the instructions are executed by the processor, the electronic device is enabled to: identifying candidate speeds associated with the vehicles based on the vehicle-related information respectively obtained from the vehicles; as well as The lowest candidate speed among the candidate speeds is determined as the speed for the vehicle to travel on the curved road section.

4. The electronic device according to claim 1, wherein: The vehicle includes a first vehicle and a second vehicle, When the instructions are executed by the processor, the electronic device is enabled to: determining a distance between the first vehicle and the second vehicle based on the risk-related information of the first vehicle and the risk-related information of the second vehicle; The distance is transmitted to the first vehicle and the second vehicle.

5. 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 determined speed, a reference speed is set; Based on the reference speed, each of the vehicles is divided into one of a first group and a second group; The moving paths of the vehicles in the first group are set as the inner lane of the curved road section, and the moving paths of the vehicles in the second group are set as the outer lane of the curved road section.

6. The electronic device according to claim 1, wherein: The vehicle comprises: a first vehicle; a second vehicle following the first vehicle; and a third vehicle following the second vehicle, When the instructions are executed by the processor, the electronic device is enabled to: Setting the movement paths of the first vehicle and the third vehicle, whose lengths are less than or equal to a specified length, as outer lanes of the curved road section; The moving path of the second vehicle, which is longer than the specified length, is set as the inner lane of the curved road section. The distance between the first vehicle and the third vehicle is set to be greater than the length of the second vehicle.

7. The electronic device according to claim 1, wherein: When the instructions are executed by the processor, the electronic device is enabled to: Before the vehicles enter the curved road section, identifying the formation of the vehicles; storing information about the formation of the vehicles in the memory; Based on all the vehicles leaving the curved road section, the vehicles are controlled to form the formation.

8. The electronic device according to claim 1, wherein: When the instructions are executed by the processor, the electronic device is enabled to: identifying information about the path that the vehicles performing group driving are about to enter based on an electronic map; acquiring an image of the path using the camera; Based on the information of the path and the image, curvature information of the path is acquired.

9. The electronic device according to claim 1, wherein: The vehicle includes a truck comprising a tractor and a trailer, The weight of the truck is identified as the sum of the weight of the tractor and the weight of the trailer, The length of the truck is identified as the sum of the length of the tractor and the length of the trailer.

10. The electronic device according to claim 1, wherein: The electronic device is included in a lead vehicle among the vehicles.

11. A method for an electronic device for cluster driving of vehicles, comprising the following operations: Obtaining curvature information of a path that the vehicles performing cluster driving are about to enter; Based on the curvature information, identifying a curved section on the path having a curvature greater than or equal to a reference curvature; acquiring vehicle-related information including at least one of a weight of the vehicle or a length of the vehicle from each of the vehicles; Using the vehicle-related information obtained from each vehicle and the curvature of the curved road section, obtaining risk-related information for each vehicle when traveling on the curved road section; determining a speed for the vehicle to travel on the curved road section based on the risk-related information; as well as The determined speed is respectively transmitted to the vehicles.

12. The method according to claim 11, wherein It also includes the following operations: Determining a movement path for the vehicle to travel on the curved road section based on the risk-related information; and The determined speed and the moving path are respectively sent to the vehicles.

13. The method according to claim 11, wherein It also includes the following operations: identifying candidate speeds associated with the vehicles based on vehicle-related information obtained from each of the vehicles; and The lowest candidate speed among the candidate speeds is determined as the speed for the vehicle to travel on the curved road section.

14. The method according to claim 11, wherein The vehicle includes a first vehicle and a second vehicle, The method further includes the following operations: determining a distance between the first vehicle and the second vehicle based on the risk-related information of the first vehicle and the risk-related information of the second vehicle; and The distance is transmitted to the first vehicle and the second vehicle.

15. The method according to claim 11, wherein It also includes the following operations: Based on the determined speed, a reference speed is set; Based on the reference speed, each of the vehicles is divided into one of a first group and a second group; as well as The moving paths of the vehicles in the first group are set as the inner lane of the curved road section, and the moving paths of the vehicles in the second group are set as the outer lane of the curved road section.

16. The method according to claim 11, wherein The vehicle comprises: a first vehicle; a second vehicle following the first vehicle; and a third vehicle following the second vehicle, The method further includes the following operations: Setting the movement paths of the first vehicle and the third vehicle, whose lengths are less than or equal to a specified length, as outer lanes of the curved road section; and The moving path of the second vehicle, which is longer than the specified length, is set as the inner lane of the curved road section. The distance between the first vehicle and the third vehicle is set to be greater than the length of the second vehicle.

17. The method according to claim 11, wherein It also includes the following operations: Before the vehicles enter the curved road section, identifying the formation of the vehicles; storing information about the formation of the vehicles in the memory; as well as Based on all the vehicles leaving the curved road section, the vehicles are controlled to form the formation.

18. The method according to claim 11, wherein It also includes the following operations: identifying information about the path that the vehicles performing group driving are about to enter based on an electronic map; acquiring an image of the path using the camera; as well as Based on the information of the path and the image, curvature information of the path is acquired.

19. The method according to claim 11, wherein The vehicle includes a truck comprising a tractor and a trailer, The weight of the truck is identified as the sum of the weight of the tractor and the weight of the trailer, The length of the truck is identified as the sum of the length of the tractor and the length of the trailer.

20. A non-transitory computer-readable storage medium for storing one or more programs, the one or more programs comprising instructions that, when executed by a processor of an electronic device, are capable of: Obtaining curvature information of the path that the vehicles executing cluster driving are about to enter; Based on the curvature information, identifying a curved section on the path having a curvature greater than or equal to a reference curvature; acquiring vehicle-related information including at least one of a weight of the vehicle or a length of the vehicle from each of the vehicles; Using the vehicle-related information obtained from each vehicle and the curvature of the curved road section, obtaining risk-related information for each vehicle when traveling on the curved road section; determining a speed for the vehicle to travel on the curved road section based on the risk-related information; The determined speed is respectively transmitted to the vehicles.

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