Apparatus for controlling autonomous driving and intersection control method thereof

Abstract

A device for controlling autonomous driving and a merging control method thereof, the device for controlling the autonomous driving of a master vehicle may include a processor and a memory, the memory storing at least one instruction, the at least one instruction being configured, when executed by the processor communicating with the memory, to cause the device to select at least one target candidate vehicle expected to enter a merging section of road on which the master vehicle is driving; determine a target yield level associated with the at least one target candidate vehicle, wherein the target yield level corresponds to indicating the probability that the master vehicle will generate the at least one target candidate vehicle, selecting a final target vehicle based on the determined target yield level, outputting a signal, and controlling the autonomous driving of the master vehicle.

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit of priority to Korean Patent Application No. 10-2024-0189556, filed with the Korean Intellectual Property Office on December 18, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to an autonomous driving control device and an autonomous driving control method in a road merging section, and more specifically, to a technique for controlling an autonomous vehicle to give way to a vehicle entering a merging road in a road merging section. Background Technology

[0004] The descriptions in this background section are intended only to enhance the understanding of the background techniques of this disclosure and should not be construed as an admission that they correspond to prior art known to those skilled in the art.

[0005] An autonomous vehicle is a vehicle that, while driving, uses external information detection and processing functions to identify its surrounding environment, determine its own driving path, and drive independently using its own power. An autonomous vehicle is an intelligent vehicle equipped with automatic driving technology, which allows the vehicle to reach its destination without the driver needing to directly operate the steering wheel, accelerator pedal, or brakes.

[0006] Roads can be classified into various types based on their shape, size, and function, but they can generally be configured to branch off from one road into multiple roads or to merge multiple roads into one road.

[0007] When multiple roads are constructed to merge into one road, vehicles entering the merging road can give way to each other and travel on a single road.

[0008] However, autonomous vehicles may not have a separate strategy for entering merging lanes in merging sections of a road, or they may not drive in a similar manner to the vehicles in front.

[0009] Therefore, strategies and response measures are considered to enable autonomous vehicles to drive more effectively when navigating in merging zones. Summary of the Invention

[0010] In addition, it can provide various effects that can be directly or indirectly identified through this specification.

[0011] According to this disclosure, an apparatus for controlling the autonomous driving of a master vehicle may include a processor and a memory storing at least one instruction, wherein the at least one instruction, when executed by the processor in communication with the memory, is configured to cause the apparatus to select at least one target candidate vehicle expected to enter a road merging segment in which the master vehicle is traveling, determine a target yield level associated with the at least one target candidate vehicle, wherein the target yield level corresponds to a value indicating the probability that the master vehicle will generate the at least one target candidate vehicle, select a final target vehicle from the at least one target candidate vehicle based on the determined target yield level, output a signal based on the selected final target vehicle, and control the autonomous driving of the master vehicle based on the signal to generate the final target vehicle.

[0012] The device, wherein the at least one instruction, when executed by the processor communicating with the memory, is configured to cause the device to select the final target vehicle from at least one target candidate vehicle based on at least one of the following: the speed of the master vehicle, information about objects within a threshold distance of the master vehicle, or information about the merging section of the road merging section. The device further includes the device where the information about the object may include the object's location or speed.

[0013] The device, wherein the merging section information may include at least one of the following: the starting point of the merging section, the ending point of the merging section, the type of the merging section, or the direction of travel of the merging section.

[0014] The device, wherein when the at least one instruction is executed by the processor communicating with the memory, the at least one instruction is configured to cause the device to select the vehicle as a target candidate vehicle based on the vehicle traveling in a lane adjacent to the lane in which the master vehicle is traveling and the risk of collision with the master vehicle at the end of the merging section of the road, wherein the collision risk exceeds a predetermined reference value.

[0015] The device, wherein the at least one instruction, when communicating with the memory by the processor, is configured to cause the device to determine, based on at least one of a predetermined maximum yield level, a predetermined minimum yield level, a minimum time threshold for the at least one target candidate vehicle to reach the merging endpoint, a maximum time threshold for the at least one target candidate vehicle to reach the merging endpoint, or the current time remaining until the at least one target candidate vehicle reaches the merging endpoint.

[0016] The device, wherein the at least one instruction is configured, when the processor communicates with the memory, to cause the device to determine a final yield level based on at least one of the target yield level, a predetermined maximum adjustment factor applied to the target yield level, a predetermined minimum adjustment factor applied to the target yield level, a predetermined maximum speed of the master vehicle, a predetermined minimum speed of the master vehicle, or the current speed of the master vehicle.

[0017] The device, wherein when the at least one instruction is executed by the processor communicating with the memory, the at least one instruction is configured to cause the device to determine a final yield level for each of the at least one target candidate vehicles, and to generate a speed profile of a target vehicle for tracking each of the at least one target candidate vehicles based on a control target distance, wherein the control target distance is defined as a vehicle-to-vehicle distance maintained between the master vehicle and the target vehicle, and wherein the control target distance is determined based on the final yield level and a deceleration adjustment parameter, wherein the deceleration adjustment parameter is used to adjust the deceleration rate of the master vehicle during the tracking, and wherein the deceleration adjustment parameter is determined based on the final yield level.

[0018] The device, wherein when the at least one instruction is executed by the processor communicating with the memory, the at least one instruction is configured to cause the device to select a target candidate vehicle having a speed curve and a minimum acceleration average value for the predetermined time period as a final target candidate vehicle by comparing the speed curves generated for each of the at least one target candidate vehicles with a minimum acceleration average value for the predetermined time period.

[0019] The device wherein the speed curve having the minimum average acceleration is selected from the speed curve for tracking the speed of the event target, the speed curve for tracking the maximum operating speed, and the speed curve for minimum risk manipulation (MRM).

[0020] The device, wherein the speed profile used to track the target speed of an event may include a speed profile generated for tracking in event conditions, wherein the event conditions may include entering a curved road segment and entering the merging section of the road, and the speed profile used to track the maximum operating speed may include a speed profile based on the maximum operating speed, wherein the maximum operating speed may include at least one of a road speed limit or the design maximum speed of an autonomous driving system.

[0021] According to this disclosure, a method performed by a device for controlling the autonomous driving of a master vehicle may include: selecting at least one target candidate vehicle expected to enter a road merging section on which the master vehicle is driving; determining a target yield level associated with the at least one target candidate vehicle, wherein the target yield level corresponds to a value indicating the probability that the master vehicle will generate the at least one target candidate vehicle; selecting a final target vehicle from the at least one target candidate vehicle based on the determined target yield level; outputting a signal based on the selected final target vehicle; and controlling the autonomous driving of the master vehicle based on the signal to generate the final target vehicle.

[0022] The method, wherein selecting the final target vehicle may include: selecting the final target vehicle from at least one target candidate vehicle based on at least one of the following: the speed of the primary vehicle, information related to objects within a threshold distance of the primary vehicle, or information related to merged road segments.

[0023] The method wherein the information about the object may include the object’s position or speed, and the merging section information may include at least one of the starting point of the merging section, the ending point of the merging section, the type of the merging section, or the direction of travel of the merging section.

[0024] The method, wherein the selection of the final target vehicle may include: selecting vehicles as target candidate vehicles based on vehicles traveling in lanes adjacent to the lane in which the master vehicle is traveling and vehicles that have a risk of colliding with the master vehicle at the end of the merging section of the road, wherein the collision risk exceeds a predetermined reference value.

[0025] The method, wherein determining the target yield level associated with the at least one target candidate vehicle may include: determining the target yield level based on at least one of a predetermined maximum yield level, a predetermined minimum yield level, a minimum time threshold for the at least one target candidate vehicle to reach the merging endpoint, a maximum time threshold for the at least one target candidate vehicle to reach the merging endpoint, or the current time remaining until the at least one target candidate vehicle reaches the merging endpoint.

[0026] The method, wherein determining the target yield level associated with the at least one target candidate vehicle may further include: determining a final yield level based on at least one of the target yield level, a predetermined maximum adjustment factor applied to the target yield level, a predetermined minimum adjustment factor applied to the target yield level, a predetermined maximum speed of the master vehicle, a predetermined minimum speed of the master vehicle, or the current speed of the master vehicle.

[0027] The method, wherein selecting a final target vehicle may include: selecting a target candidate vehicle as a final target candidate vehicle by selecting the speed curve of the speed curve generated for each of the at least one target candidate vehicle that has the minimum average acceleration over the predetermined time period, for tracking the target candidate vehicle.

[0028] According to this disclosure, an apparatus for controlling the autonomous driving of a vehicle may include a processor and a memory storing at least one instruction, which, when executed by the processor in communication with the memory, is configured to cause the apparatus to select at least one target candidate vehicle predicted to enter the merging road on which the vehicle is traveling, based on at least one of object information associated with the vehicle or merging segment information associated with the merging road; determine a yield level associated with each of the at least one target candidate vehicle based on at least one of collision risk or remaining time until the at least one target candidate vehicle reaches the end of the merging road, wherein the yield level corresponds to a value indicating the probability that the vehicle yields to the at least one target candidate vehicle; select a target vehicle from the at least one target candidate vehicle based on the determined yield level; output a signal indicating the selected target vehicle; and control the autonomous driving of the vehicle to yield to the selected target vehicle based on the signal.

[0029] The device, wherein the object information may include at least one of the position or speed of an object within the threshold range of the vehicle, the merging section information may include at least one of the start point, end point, type or direction of travel of the merging road, the selected target vehicle has a speed curve with a minimum average acceleration over a predetermined time period, and the determination of the yield level is also based on at least one of a predetermined maximum yield level or a predetermined minimum yield level. Attached Figure Description

[0030] Figure 1 An example configuration of a vehicle system including an autonomous driving control device is shown.

[0031] Figure 2 An example of a road merging section is shown.

[0032] Figure 3 An example speed curve of an autonomous driving control device is shown.

[0033] Figure 4 An example control method for road merging segments used in autonomous driving control devices is shown.

[0034] Figure 5 An example computing system is shown. Detailed Implementation

[0035] In the following, some examples of this disclosure will be described in detail with reference to the exemplary accompanying drawings. It should be noted that when adding reference numerals to the constituent elements of the various drawings, they include as many identical reference numerals as possible, even if the same constituent elements are shown in different drawings. In describing examples of this disclosure, descriptions of well-known configurations or functions associated with examples of this disclosure will be omitted where detailed descriptions are determined to obscure the gist of the disclosure.

[0036] In describing the constituent elements according to the examples of this disclosure, terms such as first, second, A, B, (a), and (b) may be used. These terms are used only to distinguish constituent elements from other constituent elements, and the nature, sequence, or order of the constituent elements is not limited by these terms. Furthermore, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the examples of this disclosure pertain. Terms defined in commonly used dictionaries should be interpreted as having a meaning matching their meaning in the context of the prior art and should not be interpreted as having an idealized or overly formal meaning unless they are clearly defined in this specification.

[0037] For the purposes of this application and claims, the exemplary phrases “at least one: A; B; or C” or “at least one: A; B; C” are used, which means “at least one A, or at least one B, or at least one C, or at least one A, at least one B, and at least one C.” Furthermore, as used herein, exemplary phrases such as “A, B, or C,” “at least one of A, B, and C,” “at least one of A, B, or C,” etc., may represent each listed item or all possible combinations of listed items. For example, “at least one of A or B” may refer to (1) at least one A; (2) at least one B; or (3) at least one A and at least one B.

[0038] As used in this specification, the terms "module" or "unit" refer to software and / or hardware components, and a "module" or "unit" performs certain operations / functions / roles. However, a "module" or "unit" is not to be construed as limited to software or hardware. A "module" or "unit" may be configured to reside in addressable storage media or to execute on one or more processors. Thus, by way of example, a "module" or "unit" may include at least one of the following: software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables. The functionality provided in a component, "module," or "unit" may be combined into a smaller number of components, "modules," or "units" or further divided into additional components, "modules," or "units."

[0039] In this disclosure, a “module” or “unit” can be implemented as a processor and memory. “Processor” should be broadly interpreted to include general-purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), microcontrollers, state machines, etc. In some contexts, “processor” can refer to application-specific integrated circuits (ASICs), programmable logic devices (PLDs), or field-programmable gate arrays (FPGAs). For example, “processor” can refer to a combination of processing devices, such as a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors combined with a DSP core, or any other such combination. Furthermore, “memory” should be broadly interpreted to include any electronic component capable of storing electronic information. “Memory” can refer to various types of processor-readable media, such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic or optical data storage devices, and registers. The memory can be in a state of electronic communication with the processor when the processor can read information from the memory and / or record information in the memory. Memory integrated into the processor is in a state of electronic communication with the processor.

[0040] One or more features described herein can be provided as a computer program stored in a computer-readable recording medium for execution on a computer. The medium may continuously store a computer-executable program or temporarily store a program for execution or download. Furthermore, the medium can be a variety of recording or storage devices in the form of a single hardware device or multiple combined hardware devices, and is not limited to media directly connected to certain computer systems, but may also be distributed across a network. Examples of such media include magnetic media such as hard disks, floppy disks, or magnetic tapes; optical recording media such as CD-ROMs or DVDs; magneto-optical media such as floppy disks; and ROMs, RAMs, or flash memory configured to store program instructions. Additional examples of such media include media or storage media managed by application stores that distribute applications or by different other sites or servers that provide or distribute software.

[0041] In a hardware implementation, the processing unit for performing these techniques may be implemented within one or more ASICs, DSPs, digital signal processing devices, programmable logic devices, field-programmable gate arrays, processors, controllers, microcontrollers, microprocessors, electronic devices, or computers or combinations thereof, designed to perform the functions described in this disclosure.

[0042] According to the Society of Automotive Engineers (SAE), the automation levels of autonomous vehicles can be categorized as follows: At Level 0 of Automated Driving, the SAE classification corresponds to "No Automated Operation," where the automated driving system temporarily handles emergency situations (e.g., automatic emergency braking) and / or only provides warnings (e.g., blind spot warning, lane departure warning, etc.) and anticipates driver intervention. At Level 1 of Automated Driving, the SAE classification corresponds to "Driver Assistance," where the system performs some driving functions (e.g., steering, acceleration, braking, lane centering, adaptive cruise control, etc.) when the driver operates the vehicle in normal operating conditions, and expects the driver to determine the system's operating status and / or timing, perform other driving functions, and handle (e.g., resolve) emergency situations. At Level 2 of Automated Driving, the SAE classification corresponds to "Partial Automation," where the system performs steering, acceleration, and / or braking under driver supervision, and expects the driver to determine the system's operating status and / or timing, perform other driving functions, and handle (e.g., resolve) emergency situations. At Level 3 of Automated Driving, the SAE classification standard can correspond to "Conditional Automation," where the system drives the vehicle under limited conditions (e.g., performing driving functions such as steering, acceleration, and / or braking), but transfers driving control to the driver when the desired conditions are not met. The driver is expected to determine the system's operating state and / or timing and take over control in emergency situations, but not otherwise operate the vehicle (e.g., steering, acceleration, and / or braking). At Level 4 of Automated Driving, the SAE classification standard can correspond to "High Automation," where the system performs all driving functions and the driver is expected to control the vehicle only in emergency situations. At Level 5 of Automated Driving, the SAE classification standard can correspond to "Full Automation," where the system performs all driving functions without any assistance from the driver, including in emergency situations, and the driver is not expected to perform any driving functions other than determining the system's operating state. While this disclosure can apply the SAE classification standard to automated driving classification, other classification methods and / or algorithms can be used in one or more configurations described herein. One or more features associated with automated driving control can be activated based on the configured automated driving control settings (e.g., based on at least one of the following: automated driving classification, selection of the vehicle's automated driving level, etc.). Based on one or more features described herein (e.g., features of automated driving control in road merging segments), vehicle operation can be controlled. Vehicle control may include various operational controls associated with the vehicle (e.g., automated driving control, sensor control, braking control, braking time control, acceleration control, acceleration rate of change control, warning timing control, forward collision warning timing control, etc.).

[0043] One or more auxiliary devices (e.g., engine braking, exhaust braking, hydraulic decelerator, electric decelerator, regenerative braking, etc.) may also be controlled, for example, based on one or more features described herein (e.g., features of automated driving control in road merging segments).

[0044] For example, based on one or more features described herein (e.g., features of autonomous driving control in road merging segments), one or more communication devices (e.g., modems, network adapters, radio transceivers, antennas, etc.) capable of communicating via one or more wired or wireless communication protocols such as Ethernet, Wi-Fi, Near Field Communication (NFC), Bluetooth, Long Term Evolution (LTE), 5G New Radio (NR), Vehicle to Everything (V2X), etc., can also be controlled.

[0045] Minimum Risk Maneuvering (MRM) operations can also be controlled, for example, based on one or more features described herein (e.g., features of automated driving control in merging sections of roads). Minimum Risk Maneuvering operations (e.g., minimum risk maneuver, minimum risk control) can be maneuvering operations of the vehicle to minimize (e.g., reduce) the risk of collision with surrounding vehicles in order to achieve a reduced (e.g., minimum) risk state. Minimum Risk Maneuvering can be an operation activated during automated driving when the driver is unable to respond to an intervention request. During Minimum Risk Maneuvering, one or more processors of the vehicle can control the driving operations of the vehicle for a set period of time.

[0046] One or more biased driving operations can also be controlled, for example, based on one or more features described herein (e.g., features of automated driving control in merging sections of roads). The drive control device can perform biased drive control. To perform biased driving, the drive control device can control the vehicle to travel within the lane by maintaining a lateral distance between the vehicle's center position and the center of the lane. For example, the drive control device can control the vehicle to remain in the lane but not in the center of the lane. The drive control device can identify or determine a target lateral distance for biased drive control. For example, the target lateral distance for bias can include an intentionally adjusted lateral distance that the vehicle can maintain from a reference point (such as the center of the lane or another vehicle) during maneuvers such as lane changes. This adjustment can be made to improve the vehicle's stability, safety, and / or performance under changing driving conditions. For example, during lane changes, the drive control system can bias the lateral distance to maintain a safer clearance with adjacent vehicles, taking into account factors such as vehicle speed, road conditions, and / or the presence of obstacles.

[0047] One or more sensors (e.g., IMU sensors, cameras, LiDAR, RADAR, blind spot monitoring sensors, lane departure warning sensors, parking sensors, light sensors, rain sensors, traction control sensors, anti-lock braking system sensors, tire pressure monitoring sensors, seat belt sensors, airbag sensors, fuel sensors, emission sensors, throttle position sensors, inverters, converters, motor controllers, power distribution units, high-voltage wiring and connectors, auxiliary power modules, charging interfaces, etc.) can also be controlled, for example, based on one or more features described herein (e.g., features of autonomous driving control in merging sections of roads). Operational control for autonomous vehicles may include various driving controls of the vehicle by vehicle control equipment (e.g., acceleration, deceleration, steering control, gear shifting control, braking system control, traction control, stability control, cruise control, lane keeping assist control, collision avoidance system control, emergency braking assist control, traffic sign recognition control, adaptive headlight control, etc.).

[0048] In the following text, reference will be made to Figures 1 to 5 Various embodiments of this disclosure are described in detail.

[0049] Figure 1 An example configuration of a vehicle system including an autonomous driving control device is shown.

[0050] refer to Figure 1 The vehicle system according to the example of this disclosure can be configured to include an autonomous driving device 100, a sensing device 200, and a map database 300.

[0051] According to the examples of this disclosure, the autonomous driving control device 100 may be implemented within a vehicle or independently of the vehicle. In this case, the autonomous driving control device 100 may be integrally formed with the vehicle's internal control unit, or it may be implemented as a separate hardware device connected to the vehicle's control unit via a connection device. For example, the autonomous driving control device 100 may be implemented integrally with the vehicle, mounted on the dashboard, under the seat, or within the control panel, or it may be implemented as a vehicle-independent construction to be mounted or attached to the vehicle, or a portion of it may be implemented integrally with the vehicle, and another portion may be implemented as a vehicle-independent construction to be mounted or attached to the vehicle. In some cases, the device may be embedded in telematics circuitry, electronic control circuitry, or a central computing platform, etc.

[0052] The autonomous driving control device 100 can be configured to perform autonomous driving control naturally according to traffic flow by implementing a yield control strategy on surrounding objects in the merging section of the road.

[0053] The autonomous driving control device 100 can be configured to: select at least one target candidate vehicle, such as a passenger car, truck, motorcycle, or bus, which is expected or predicted to enter the merging lane of the main vehicle at a merging segment of the road; select a final target vehicle from the at least one target candidate vehicle by determining the target level of the at least one target candidate vehicle; and control the main vehicle to give way to the final target vehicle in response to the final target vehicle attempting to enter the merging lane of the main vehicle at the end of the merging segment of the road. For example, this yielding may occur based on real-time assessments of vehicle speed, lane geometry, or estimated collision time.

[0054] In this scenario, a target candidate vehicle can be designated as a vehicle traveling within a merging segment and entering the merging lane of the main vehicle at the end of the merging segment. This merging segment is adjacent to the lane the main vehicle is traveling in, but the risk of collision between this vehicle and the main vehicle exceeds a predetermined threshold and is likely to be caused by the main vehicle. This target candidate vehicle is referred to below as a yield target candidate vehicle. The risk level can be calculated based on predicted collision time, lane overlap probability, or relative speed thresholds. Furthermore, a final target vehicle can be designated as a vehicle among the yield target candidates that the main vehicle has decided to yield to. This final yield target vehicle is referred to below as the final yield target vehicle, and the main vehicle can be controlled to yield in response to the final yield target vehicle entering the merging lane. This yielding action may include slowing down, adjusting lateral position, or temporarily halting acceleration. Additionally, the vehicle's target level can be a yield level, and the target level can increase as the probability of yielding increases. This is referred to below as the vehicle's yield level. The yield level can be dynamically adjusted based on environmental context (e.g., road curvature, merging angle, or visibility). Examples of vehicles that can yield to a target vehicle may include those that perform sudden acceleration, stay close to the merging point or signal their intention to merge, actively change lanes, or match the speed of the primary vehicle to force a merging.

[0055] The autonomous driving control device 100 may include a communication device 110, a storage device 120, an interface device 130, and a processor 140. According to one example of this disclosure, the autonomous driving control device 100 can be implemented as a single unit by coupling the components together, and some components may be omitted. For example, in a lightweight system configuration, the interface device 130 may be omitted, or the communication device 110 may be integrated with the processor 140.

[0056] The communication device 110 may be a hardware device implementing various electronic circuits to transmit and receive signals via wireless or wired connections, and may be configured to transmit and receive information based on in-vehicle devices and in-vehicle network communication technologies. As an example of this disclosure, in-vehicle network communication technologies may include Controller Area Network (CAN) communication, Local Interconnect Network (LIN) communication, Flexible Ray Communication, Ethernet, or MOST (Media-Oriented System Transmission), etc.

[0057] The communication device 110 may be a hardware device implemented with various electronic circuits to transmit and receive signals via wireless or wired connections, and may be configured to perform communication with in-vehicle devices. For example, the communication device 110 may be configured to receive data from the sensing device 200 and the map database 300. In some cases, the communication device may interface with external devices such as cloud servers, remote diagnostic tools, or over-the-air (OTA) update platforms.

[0058] As an example of this disclosure, in-vehicle network communication technologies may include Controller Area Network (CAN) communication, Local Interconnect Network (LIN) communication, Flexible Ray Communication, Flexible Ray Communication, or other automotive-specific communication protocols.

[0059] Communication device 110 can be configured to perform V2X communication. V2X communication can include communication between a vehicle and all entities, such as V2V (vehicle-to-vehicle) communication (communication between vehicles), V2I (vehicle-to-infrastructure) communication (communication between a vehicle and an eNB or roadside unit (RSU), V2P (vehicle-to-pedestrian) communication (Communication between a user equipment (UE) held by a vehicle and an individual (e.g., a pedestrian, cyclist, vehicle driver, or passenger),) and V2N (vehicle-to-network) communication. For example, communication device 110 can be configured to receive information such as the speed and location of vehicles driving in a merging segment by communicating with surrounding vehicles driving in the merging segment. This information can be used to assess merging risk, predict intent, or determine yielding priorities, etc.

[0060] In addition, the communication device 110 may include a mobile communication module, a wireless internet module, a short-range communication module, etc., for communicating with external systems such as cloud platforms, traffic control centers, or navigation services.

[0061] The mobile communication module can be configured to perform communication using technical standards or communication methods for mobile communication (e.g., Global System for Mobile Communications (GSM), Code Division Multiple Access (CDMA), Code Division Multiple Access 2000 (CDMA 2000), Enhanced Voice Data Optimized or Enhanced Voice Data Only (EV-DO), Wideband CDMA (WCDMA), High-Speed ​​Downlink Packet Access (HSDPA), High-Speed ​​Uplink Packet Access (HSUPA), Long Term Evolution (LTE), LTE-A Advanced, 4G, 5G, etc.).

[0062] A wireless internet module is a module used for wireless internet access and can be configured to communicate via technologies such as Wireless LAN (WLAN), Wi-Fi, Wi-Fi Direct, Digital Living Network Alliance (DLNA), WiBro, WiMAX, High-Speed ​​Downlink Packet Access (HSDPA), High-Speed ​​Uplink Packet Access (HSUPA), LTE, LTE-A Advanced, Bluetooth network sharing, cellular hotspots, or similar technologies. Wireless internet modules can support real-time map updates, streaming vehicle diagnostics, and cloud-based decision support.

[0063] The short-range communication module can use Bluetooth. TM It uses at least one of the following technologies: Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wideband (UWB), ZigBee, Near Field Communication (NFC), Wireless Universal Serial Bus (USB), or any combination thereof, to support short-range communication. This communication can be used for keyless entry, mobile device pairing, or local V2V coordination, etc.

[0064] Storage device 120 can be configured to store sensing results from sensing device 200 and data and / or algorithms for operation of processor 140, including control logic, environmental models, or machine learning parameters.

[0065] For example, storage device 120 may be configured to store maximum level, minimum level, and the minimum time remaining until the yielding candidate vehicle reaches the end of the yielding segment, and the maximum time remaining until the yielding candidate vehicle reaches the end of the yielding segment, etc., which are predetermined for level determination. Furthermore, storage device 120 may be configured to store predetermined maximum gain, minimum gain, maximum master vehicle speed, minimum master vehicle speed, etc., for final level determination. In this case, level can represent the degree of yielding. These parameters can be empirically derived from driving data, safety margins, or regulatory standards.

[0066] Storage device 120 may include at least one type of storage medium such as flash memory, hard disk, microSD card (e.g., Security Digital (SD) card, Ultimate Digital (XD) card, or MicroSD card), random access memory (RAM), static RAM (SRAM), read-only memory (ROM), programmable ROM (PROM), electrically erasable PROM (EEPROM), magnetic RAM (MRAM), magnetic disk, and optical disk. The choice of storage medium may depend on system constraints such as speed, durability, cost, or environmental tolerance.

[0067] Interface device 130 may include an input device for receiving control commands from a user and an output device for outputting the operating state and results of output device 100. Here, the input device may include buttons, and may include a mouse, joystick, jog shuttle, stylus, touchpad, or rotary controller, etc. Furthermore, the input device may include soft keys implemented on the display. In some configurations, voice recognition and gesture input systems may also be used as input devices.

[0068] The interface device 130 may be implemented as a head-up display (HUD), cluster, audio-visual navigation (AVN), or human-machine interface (HM) or human-machine interface (HMI) (e.g., digital cockpit, central control panel, or infotainment system).

[0069] The output device may include a display and may also include a voice output device such as a speaker. In this case, in response to a touch sensor formed by a touch film, touch sheet, or touchpad being disposed on the display, the display may operate as a touchscreen and may be implemented in the form of an integrated input and output device. In this disclosure, the output device may output information such as the autonomous vehicle's driving status, the driving route to the destination, the yield control status in merging sections, system alarms, or real-time traffic and map updates.

[0070] In this context, the display may include at least one of a liquid crystal display (LCD), a thin-film transistor liquid crystal display (TFTLCD), an organic light-emitting diode display (OLED display), a flexible display, a field emission display (FED), and a 3D display. For example, holographic or curved displays may also be used for immersive visualization, etc.

[0071] The processor 140 may be electrically connected to the communication device 110, storage device 120, interface unit 130, and other internal or external modules, and is configured to perform overall control so that each component can perform its function correctly. Furthermore, the processor 140 may be a circuit configured to electrically control each component and execute software commands to perform various data processing and calculations described later. Such processing may include object recognition, path planning, risk assessment, or speed curve generation, etc.

[0072] Processor 140 can be implemented in hardware, software, or a combination of both. Processor 140 can be implemented using application-specific integrated circuits (ASICs), digital signal processors (DSPs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), central processing units (CPUs), microcontrollers, microprocessors, etc., but this disclosure is not limited thereto. For example, it can be an electronic control unit (ECU), microcontroller unit (MCU), or other sub-controller installed in a vehicle. In other implementations, the processor can be distributed across multiple computing platforms, such as vehicle domain controllers, edge servers, or AI accelerators.

[0073] The processor 140 can be configured to select at least one yield target candidate vehicle that is expected or predicted to enter the merging road where the main vehicle is traveling in the merging section of the road, select a final yield target vehicle from the at least one yield target candidate vehicle by determining the yield level of the at least one yield target candidate vehicle, and perform yield control of the final yield target vehicle. This control may involve adjusting acceleration, deceleration, or gap distance to safely accommodate merging behavior, etc.

[0074] In this scenario, the yield target vehicle (which is a vehicle traveling in the merging section, which is a lane adjacent to the lane the main vehicle is traveling in) can indicate the vehicle expected or predicted to enter the main vehicle's travel lane at the end of the merging section, 201, and Figure 2 In this context, the first object 104 and the second object 105 can be candidate vehicles for yielding. Figure 2 An example of a merging section of a road is shown. For example, merging vehicles may include vehicles accelerating up a ramp, motorcycles weaving in and out of lanes, or heavy trucks entering from the right-hand merging lane.

[0075] Depending on road design and traffic flow characteristics, merging sections can take various forms. For example, at defined distances common on highways and expressways, tapering merging sections gradually narrow lanes into adjacent lanes. Roundabout merging sections connect curved non-ramp or ramped sections to the main road, typically requiring vehicles to merge at lower speeds. Merging acceleration lanes (such as those found at highway entrances) allow entering vehicles to accelerate before merging with the main traffic. In urban environments, ramps or feeder roads are frequently used to divert traffic from service roads or feeder roads to the main road. Additionally, roundabout exits (where two exit lanes converge into one downstream lane) or built-up areas with temporary lane reductions can also serve as merging sections. These varied configurations produce different merging dynamics that autonomous driving systems can recognize and adapt to.

[0076] Furthermore, the final yield target vehicle can be selected as the target vehicle, and the processor 140 can be configured to generate a speed profile for tracking the target vehicle. The speed profile can take into account desired inter-vehicle distance, acceleration constraints, or traffic flow dynamics, etc.

[0077] The processor 140 can be configured to select at least one yield target candidate vehicle by using at least one of the following: the speed of the primary vehicle, surrounding object information, merging section information, or a combination thereof. In this case, the surrounding object information may include the location or speed of objects surrounding the primary vehicle, and the merging section information may include at least one of the following: the start point of the merging section, the end point of the merging section, the type of the merging section, the direction of travel of the merging section, or a combination thereof. For example, the merging section type may include a cone merging, a ramp merging, or a roundabout entrance.

[0078] The processor 140 can be configured to select a vehicle as a yield target candidate vehicle in response to a situation where the risk of a vehicle merging in a merging segment and colliding with a primary vehicle at the end of the merging segment exceeds a predetermined benchmark. This merging segment is a lane adjacent to the lane in which the primary vehicle is traveling. Such benchmarks may be based on time-of-collision (TTC), projected lateral displacement, or combined velocity vector analysis, etc.

[0079] The processor 140 can be configured to determine the yield level by using at least one of a predetermined maximum yield level, a predetermined minimum yield level, the minimum time remaining until the predetermined yield target candidate vehicle reaches the merging endpoint, the maximum time remaining until the predetermined yield target candidate vehicle reaches the merging endpoint, and the remaining time until the yield target candidate vehicle reaches the merging endpoint, or a combination thereof. For example, the yield level can be influenced by factors such as whether the merging vehicle is actively accelerating, approaching from a curve ramp, or about to approach the main vehicle.

[0080] The processor 140 can be configured to determine a final yield level by using at least one of the determined yield level, a predetermined maximum gain, a predetermined minimum gain, a predetermined maximum main vehicle speed, a predetermined minimum main vehicle speed, a main vehicle speed, or a combination thereof. For example, the gain value used to adjust the yield level can be changed based on road conditions (e.g., wet road surface, poor visibility, or high-speed area) or vehicle characteristics (e.g., size, type, or maneuverability).

[0081] For example, processor 140 can be configured to determine the yield level as shown in Equation 1 below.

[0082] (Equation 1)

[0083]

[0084] A represents the maximum yield level, B represents the minimum yield level, C represents the minimum remaining time until the yield target candidate vehicle reaches the merging endpoint, D represents the maximum remaining time until the yield target candidate vehicle reaches the merging endpoint, and E represents the remaining time until the yield target candidate vehicle reaches the merging endpoint. In this case, the maximum yield level, minimum yield level, minimum remaining time until the yield target candidate vehicle reaches the merging endpoint, and maximum remaining time until the yield target candidate vehicle reaches the merging endpoint can be predetermined by experimental values ​​and stored in the storage device 120. Such experimental values ​​can be derived from real-world traffic data, driving simulations, or safety standards (e.g., minimum collision time thresholds, driver comfort margins, or management guidelines).

[0085] The processor 140 can be configured to divide the value (AB) obtained by subtracting the minimum yield level from the maximum yield level by a value (CD) obtained by subtracting the maximum time remaining until the target vehicle reaches the merging endpoint from the minimum time remaining until the target vehicle reaches the merging endpoint. This normalization rate can be used to dynamically scale the yield level based on how urgent the approaching merging vehicles are.

[0086] Processor 140 can be configured to select from minimum yield level (B) and value Select the maximum value (MAX) from the results. The value is obtained by multiplying it by the value (ED) obtained by subtracting the maximum remaining time until the target candidate vehicle reaches the merging endpoint from the remaining time until the target candidate vehicle reaches the merging endpoint. Where this value By using this value It is obtained by adding the minimum yield level (B). Such a formula can adapt the yielding behavior based on different driving contexts (e.g., fast-approaching vehicles, merging and intersecting traffic, or high-density traffic).

[0087] Next, the processor 140 can be configured to select a minimum value (MIN) from the selected maximum value (MAX) and the maximum yield level (A), and determine the selected minimum value (MIN) as the yield level. This step ensures that the yield level is kept within a safe and predetermined operating range, avoiding excessive or insufficient response levels.

[0088] The processor 140 can be configured to determine the final yield level by using the yield level determined in Equation 1 as shown in Equation 2 below.

[0089] (Equation 2)

[0090]

[0091] F represents the maximum gain, G represents the minimum gain, H represents the maximum main vehicle speed, I represents the minimum main vehicle speed, and J represents the main vehicle speed.

[0092] In this case, the maximum gain (F), minimum gain (G), maximum master vehicle speed (H), and minimum master vehicle speed (I) can be determined in advance through experimental values ​​and stored in the storage device 120. The master vehicle speed (J) can refer to the speed of the master vehicle while it is in motion. Such gain values ​​can be derived empirically to reflect the optimal responsiveness in different driving scenarios (e.g., merging at highway speeds, low-speed urban intersections, or congested ramps).

[0093] As described above, processor 140 can be configured to determine each final yield level for at least one yield target candidate vehicle. This enables the vehicle system to dynamically and proportionally allocate priorities across multiple merging candidates based on real-time traffic behavior.

[0094] Furthermore, the processor 140 can be configured to use a control target distance, reflecting a determined final yield level for each of the at least one yielding target candidate vehicles, and a deceleration adjustment parameter, reflecting a determined final yield level for each of the at least one yielding target candidate vehicles, to generate a speed profile for tracking the target vehicle for each of the at least one yielding candidate vehicles, such as... Figure 3 As shown. For example, a higher final yield level may result in increased following distance and a smoother deceleration curve, especially when interacting with larger or faster merging vehicles (e.g., trucks, buses, or scooters). Figure 3 An example speed curve of an autonomous driving control device is shown.

[0095] The processor 140 can be configured to select the candidate vehicle for giving way as the final candidate vehicle by comparing the average acceleration over a predetermined time period in the speed curves generated for target vehicle tracking for each of at least one candidate vehicle for giving way. For example, this comparison can be used to identify the safest and most stable vehicle to give way to, especially in complex merging scenarios (e.g., simultaneous merging, staggered lane descents, or multi-lane highways, etc.).

[0096] The processor 140 can be configured to generate speed curves for tracking a target vehicle (which selects the final yielding target vehicle as the target vehicle), speed curves for tracking the speed of an event target, speed curves for tracking the maximum operating speed, and speed curves for minimum risk maneuvering (MRM). For example, each curve can be adjusted to optimize for different objectives, such as comfort, efficiency, compliance with legal speed limits, or collision avoidance.

[0097] The processor 140 can be configured to perform yield control on the final yield target vehicle by comparing the average acceleration over a predetermined time period with each of the following speed curves: one for tracking the target vehicle, one for tracking the event target speed, one for tracking the maximum operating speed, and one for minimum risk maneuvering (MRM). This allows the processor to track the speed curve with the minimum average acceleration over the predetermined time period. This ensures that the vehicle follows the most stable and energy-efficient path while yielding, helping to reduce unnecessary acceleration-deceleration cycles.

[0098] Speed ​​profiles used to track the target speed of an event may include speed profiles used to track events including those entering curved sections and merging road sections.

[0099] The speed profile used to track the maximum operating speed may include a speed profile used to track the maximum operating speed, which includes road speed limits and the design maximum speed of the autonomous driving system. Other scenarios for the speed profile may include the detection of sudden braking of the preceding vehicle, sharp turns, lane obstacles, or nearby road users (e.g., pedestrians, cyclists, or scooters).

[0100] The sensing device 200 may include one or more sensors that sense obstacles (e.g., vehicles in front) located around the host vehicle and measure the distance to and / or the relative speed of the obstacles. Furthermore, the sensing device 200 may be configured to identify lanes, signs, road markings, traffic lights, or building zones, etc.

[0101] The sensing device 200 may include multiple sensors for sensing external objects of the vehicle to obtain information related to the position, speed, direction of movement, and / or type of the external object (e.g., vehicle, pedestrian, bicycle, or motorcycle). For this purpose, the sensing device 200 may include ultrasonic sensors, radar, cameras, laser scanners and / or angle radar, lidar, acceleration sensors, yaw rate sensors, torque measurement sensors and / or wheel speed sensors, steering angle sensors, gyroscopes, or magnetometers, etc. In this disclosure, information related to surrounding objects can be obtained through lidar, radar, cameras, infrared sensors, or sensor fusion systems combining multiple sensor inputs. Furthermore, in this disclosure, vehicle speed information can be obtained from wheel speed sensors, acceleration sensors, yaw rate sensors, or GPS-based speed estimators, etc.

[0102] The map database 300 can be configured to provide map information to the autonomous driving control device 100, and specifically, the map information may include road merging segment information. In this case, the merging segment information may include the starting point, ending point, shape, and direction of the merging segment. Additional attributes may include lane counts, merging priority rules, road curvature, elevation profile, or historical traffic flow patterns.

[0103] In the following text, reference will be made to Figure 4 Describes an autonomous driving control method according to an example of this disclosure. Figure 4 An example control method is shown in a return flow section of a road for use with an autonomous driving control device. The flowchart outlines how the device evaluates and manages merging behavior to maintain safety and driving comfort.

[0104] In the following text, it is assumed that... Figure 1 The automatic driving control device 100 performs Figure 4 The process. Furthermore, in Figure 4 In the description, the operations described as being performed by the device can be understood as being controlled by the processor 140 of the autonomous driving control device 100. In the example below, operations S101 to S106 can be performed sequentially, but not necessarily sequentially. For example, the order of each operation can be changed, and at least two operations can be performed in parallel. This flexibility allows the vehicle system to adapt to various traffic conditions (e.g., sudden lane changes, simultaneous merging, or high-speed conditions).

[0105] Reference Figure 4The autonomous driving control device 100 can be configured to select a yield target candidate vehicle based on the speed of the main vehicle, surrounding object information, and merging section information (S101). The autonomous driving control device 100 can be configured to obtain vehicle speed information from wheel speed sensors, acceleration sensors, yaw rate sensors, or GPS receivers of the sensing device 200.

[0106] Furthermore, the autonomous driving control device 100 can be configured to obtain surrounding object information through sensing devices 200 such as lidar, radar, cameras, ultrasonic sensors, or sensor fusion modules. In this case, surrounding objects may include vehicles around the vehicle (e.g., buses, trucks, vans, or public buses), pedestrians, two-wheeled vehicles (e.g., motorcycles, bicycles), buildings, traffic cones, or roadside infrastructure, and the surrounding object information may include the location of the surrounding objects, the speed of the surrounding objects, the direction of travel of the surrounding vehicles, or the object classification type.

[0107] The autonomous driving control device 100 can be configured to obtain merging segment information from the map database 300. This merging segment information may include the starting point, ending point, shape, and direction of the merging segment. Additional map attributes may include merging priority rules, lane count conversion, curvature, slope, or historical congestion levels.

[0108] like Figure 2 As shown, a yield target candidate vehicle can be selected from the vehicle 102 traveling in the lane where the main vehicle 101 is traveling and the vehicles 103 and 104 traveling in the merging section lanes. The selected vehicles can be prioritized based on factors such as the urgency of merging, proximity, or relative speed.

[0109] For example, from merging vehicles traveling in the merging lanes of the merging section, merging vehicles 103 and 104 ahead of the main vehicle can be selected as yield target candidates. Yield target candidates can indicate vehicles that are expected to require the main vehicle to yield at the end of the merging section. Other examples may include vehicles entering at high speed from an adjacent uphill, vehicles signaling their merging intention, or vehicles approaching from a blind spot area.

[0110] Subsequently, the autonomous driving control device 100 can be configured to determine whether the remaining time until the selected yielding target candidate vehicle reaches the merging endpoint is greater than a predetermined threshold (t), and in response to the case that the remaining time until the selected yielding target candidate vehicle reaches the merging endpoint is greater than the predetermined threshold (t), determine the final yielding level for each selected yielding target candidate vehicle (S102). This determination can be based on predicted vehicle trajectories, acceleration patterns, or route intent data received via V2X communication, etc. For example, in response to the case that the remaining time until the yielding target candidate vehicle 1 reaches the merging endpoint is greater than the predetermined threshold (t), the final yielding level for the yielding target candidate vehicle 1 can be determined, and in response to the case that the remaining time until the yielding target candidate vehicle 2 reaches the merging endpoint is less than or equal to the predetermined threshold (t), the final yielding level for the yielding target candidate vehicle 2 can be determined uncertain. This logic allows the vehicle system to focus computational and control resources on vehicles that cause timely merging effects, while deprioritizing more distant vehicles or those less likely to interact. This approach helps prioritize interactions between vehicles most relevant to an impending merging conflict (e.g., vehicles approaching rapidly, vehicles signaling a lane change, or vehicles entering from a highway ramp).

[0111] The automated driving control device 100 can be configured to check whether the remaining time until the yield target candidate vehicle reaches the end of the yield segment is greater than a predetermined reference time, and in response to the case where it is greater than the predetermined reference time, determine the yield level. This reference time can be dynamically adjusted based on vehicle type (e.g., sedan, SUV, or heavy truck), road conditions (e.g., wet, icy, or dry), or environmental factors (e.g., fog, glare, or night driving).

[0112] As in Equation 1, the automated driving control device 100 can be configured to determine the yield level using the remaining time until the yield target candidate vehicle reaches the end of the yield segment, and as in Equation 2, to determine the final yield level using the yield level determined in Equation 1 and the vehicle speed of the primary vehicle. This method ensures that both urgency (merging time) and feasibility (primary vehicle responsiveness) are considered when establishing yield priorities.

[0113] The automated driving control device 100 can be configured to generate a speed profile for tracking a target vehicle by using a control target distance reflecting the final yield level determined for each yield target candidate vehicle and a deceleration adjustment parameter reflecting the final yield level determined for each yield target candidate vehicle (S103). The deceleration adjustment parameter can be changed according to driving conditions (e.g., wet road, downhill slope, or stop and proceed traffic) and the type of merging vehicle (e.g., car, motorcycle, or truck).

[0114] For example, suppose there are two candidate vehicles, 1 and 2, with a yield level of 0.5 for candidate vehicle 1 and 0.7 for candidate vehicle 2. The target distance for controlling the distance between vehicles is 20m. Therefore, the target distance for candidate vehicle 1 is 20m * 0.5 = 10m, and the target distance for candidate vehicle 2 is 20m * 0.7 = 14m. This illustrates how different yield levels affect the space required for smooth interaction between the lead vehicles.

[0115] In this scenario, the control target distance for yielding candidate vehicle 1 is shorter than that for yielding candidate vehicle 2. Therefore, the control amount of the determined acceleration curve can be reduced, enabling smoother control from the driver's perspective. This reduction can decrease or minimize unnecessary braking or acceleration, contributing to passenger comfort and traffic flow stability.

[0116] However, in response to a situation where the vehicle ahead is included in the yield target candidate vehicles, the vehicle ahead can have a yield level set to 1. This ensures that the vehicle system fully respects the role of the preceding vehicle in longitudinal control, regardless of its merging behavior.

[0117] The autonomous driving control device 100 can be configured to select the final yield target vehicle by comparing the 1-second average acceleration of the speed curves of each yield target candidate vehicle (S104). This method favors the vehicle that produces the smoothest and most predictable trajectory when following (e.g., low acceleration changes, consistent speed trends, or alignment with traffic flow).

[0118] For example, in Figure 2 In the scenario where the average acceleration over one second of the speed curve of candidate vehicle 103 is 0.8 and the average acceleration over one second of the speed curve of candidate vehicle 104 is 0.7, candidate vehicle 104 can be selected as the final yield target vehicle. In this case, the final yield target vehicle can refer to the vehicle that the primary vehicle ultimately yields to. This means of transportation can exhibit the most stable and predictable behavior in terms of merging paths, lane alignment, or speed maintenance.

[0119] Then, the autonomous driving control device 100 can be configured to generate speed curves for tracking the target vehicle, speed curves for tracking the target speed of the event, speed curves for tracking the maximum operating speed, and speed curves for minimum risk maneuvering (MRM) (S105). In this case, the speed curves can indicate the distribution of speed or acceleration over time, and the method used to generate the speed curves can be the same as the method used to generate normal speed curves. This generation can utilize polynomial curve fitting, Kalman filtering, or deep learning-based trajectory prediction models, etc.

[0120] In this scenario, a speed profile can be generated for the final yield target candidate vehicle to track the target vehicle, thereby selecting the final yield target candidate vehicle as the target vehicle and tracking it. In this case, the target vehicle can include not only the final yield target vehicle, but also vehicles ahead, vehicles cutting in, leading vehicles in the queue, or emergency vehicles merging according to priority rules, etc.

[0121] In addition, the speed profile used to track the speed of an event target can refer to the speed profile used to track events in situations such as entering a curved road, entering a merging road, responding to the sudden braking of a neighboring vehicle, or reacting to a warning in a building area.

[0122] In addition, speed curves can be generated to track the maximum operating speed (e.g., road speed limits, the design maximum speed of an autonomous driving system, or dynamic speed limits based on weather or V2X alerts, etc.).

[0123] In addition, the speed profile used for Minimum Risk Maneuvering (MRM) can include values ​​for the speed at which the vehicle is controlled when driving with Minimum Risk Maneuvering (MRM). MRM can be activated in fail-safe scenarios, such as sensor degradation, software rollback mode, or detection of unstable behavior of nearby vehicles.

[0124] Therefore, the autonomous driving control device 100 can be configured to track, among the speed curves for tracking the target vehicle of the final yield target, the speed curve for tracking the speed of the event target, the speed curve for tracking the maximum operating speed, and the speed curve for minimum risk maneuvering (MRM), a speed curve with a predetermined time (e.g., 1 second) of minimum acceleration average (S106). This ensures that the main vehicle follows the smoothest trajectory, which reduces oscillations, energy loss, or discomfort to the occupants.

[0125] For example, in response to a 1-second average acceleration value of 0.5 for the speed curve used to track the target vehicle of the final yield target, a 1-second average acceleration value of 0.6 for the speed curve used to track the event target speed, a 1-second average acceleration value of 0.7 for the speed curve used to track the maximum operating speed, and a 1-second average acceleration value of 0.8 for the speed curve used for minimum risk maneuvering (MRM), the 1-second average acceleration value of the speed curve used to track the target vehicle of the final yield target can be minimized. Therefore, the autonomous driving control device 100 can be configured to control the autonomous vehicle's driving by tracking the speed curve to track the target vehicle of the final yield target. This selection process prioritizes not only safety but also efficiency and driving smoothness.

[0126] In this way, according to this disclosure, the reliability of autonomous driving control can be improved by allowing the autonomous vehicle to select a yield target from vehicles attempting to enter a merging section of the road and to control the master vehicle in response to the selected yield target candidate vehicle. Such a method helps to enhance decision-making in complex traffic conditions (e.g., highway ramps, structural bottlenecks, or multi-vehicle merging interactions).

[0127] Figure 5 An example computing system is shown.

[0128] refer to Figure 5 The computing system 1000 includes at least one processor 1100, a memory 1300, a user interface input device 1400, a user interface output device 1500, a storage device 1600, and a network interface 1700 connected via a bus 1200. This configuration can correspond to a central vehicle control circuit, an edge processing circuit, or a vehicle-mounted circuit connected to a cloud interface, etc.

[0129] Processor 1100 may be a central processing unit (CPU) or a semiconductor device that performs processing on commands stored in memory 1300 and / or storage device 1600. Memory 1300 and storage device 1600 may include various types of volatile or non-volatile storage media. For example, memory 1300 may include read-only memory (ROM) 1310 and random access memory (RAM) 1320. Other types of memory may include dynamic RAM (DRAM), synchronous DRAM (SDRAM), or non-volatile dual in-line memory (NVDIMC), etc.

[0130] Therefore, the steps of the methods or algorithms described in conjunction with the examples included herein can be directly implemented by hardware, software modules, or a combination of both executed by processor 1100. Software modules may reside in storage media (i.e., memory 1300 and / or storage device 1600), such as RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, and CD-ROMs. Examples of additional storage may include solid-state drives (SSDs), embedded multimedia cards (eMMC), or universal flash memory (UFS), etc.

[0131] An exemplary storage medium is coupled to a processor 1100, which can read information from and write information to the storage medium. Alternatively, the storage medium can be integrated with the processor 1100. The processor and storage medium can reside within a dedicated application-specific integrated circuit (ASIC). The ASIC can reside within a user terminal. Alternatively, the processor and storage medium can reside as separate components within the user terminal. In automotive systems, the processor 1100 and storage medium can be implemented as part of an advanced driver assistance system (ADAS) domain control circuitry, a telematics control (TCC) circuitry, or an autonomous driving computing platform, etc.

[0132] One example of this disclosure attempts to provide an autonomous driving control device and an autonomous driving control method in a road merging section, the device and method being capable of controlling an autonomous vehicle to drive at a level similar to that of an actual driver relative to another object entering the merging road on which the autonomous vehicle is driving in the road merging section.

[0133] One example of this disclosure attempts to provide an autonomous driving control device and an autonomous driving control method in a road merging segment, which are capable of selecting at least one yield candidate vehicle from surrounding objects during autonomous driving, determining the yield level of the at least one yield candidate vehicle, selecting the final yield vehicle, and performing yield control for the final yield vehicle.

[0134] The technical objectives of this disclosure are not limited to those mentioned above, and those skilled in the art can clearly understand other technical objectives not mentioned from the description of the claims.

[0135] An example of this disclosure provides an autonomous driving control device, including a processor configured to select at least a target candidate vehicle expected or predicted to enter a merging road in a road merging segment driven by a master vehicle, determine a target level of the at least a target candidate vehicle, select a final target vehicle among the at least a target candidate vehicle, and perform control for the final target vehicle; and a storage device configured to store data and algorithms driven by the processor.

[0136] In the examples of this disclosure, the processor may be configured to select at least one target candidate vehicle by using at least one of the following: primary vehicle speed, surrounding object information, merging section information, or a combination thereof.

[0137] In the examples disclosed herein, the surrounding object information includes the position or speed of objects around the main vehicle.

[0138] In the examples of this disclosure, merging section information may include at least one of the following: the starting point of the merging section, the ending point of the merging section, the type of the merging section, the direction of travel of the merging section, or a combination thereof.

[0139] In an example of this disclosure, the processor may be configured to select a vehicle as a target candidate vehicle in response to the presence of a vehicle traveling in a merging segment and the risk of collision with the main vehicle at the end of the merging segment exceeding a predetermined reference, the merging segment being a lane adjacent to the lane in which the main vehicle is traveling.

[0140] In examples of this disclosure, the processor may be configured to determine the level by using at least one of a predetermined maximum level, a predetermined minimum level, the minimum remaining time until a predetermined target candidate vehicle reaches the merging endpoint, the maximum remaining time until a predetermined target candidate vehicle reaches the merging endpoint, and the remaining time until a target candidate vehicle reaches the merging endpoint, or a combination thereof.

[0141] In the examples of this disclosure, the processor may be configured to determine the final level by using at least one of a level, a predetermined maximum gain, a predetermined minimum gain, a predetermined maximum master vehicle speed, a predetermined minimum master vehicle speed, a master vehicle speed, or a combination thereof.

[0142] In the examples of this disclosure, the processor may be configured to: determine the final level of each of at least one target candidate vehicle; and generate a speed profile for tracking the target vehicle for each of the at least one candidate vehicle by using a control target distance that reflects the determined final level of each of the at least one target candidate vehicle and a deceleration adjustment parameter that reflects the determined final level of each of the at least one target candidate vehicle.

[0143] In an example of this disclosure, the processor may be configured to select a target candidate vehicle as the final target candidate vehicle by comparing the average acceleration over a predetermined time period in the speed curves generated for tracking the target vehicle for each of at least one target candidate vehicle.

[0144] In the examples of this disclosure, the processor can be configured to generate a speed curve for tracking the target vehicle selected as the final target vehicle, a speed curve for tracking the speed of the event target, a speed curve for tracking the maximum operating speed, and a speed curve for minimum risk maneuvering (MRM).

[0145] In the examples of this disclosure, the processor can be configured to perform yield control on a final target vehicle that is tracking a speed curve having a minimum acceleration average over a predetermined time period by comparing the average acceleration over a predetermined time period of each of the speed curves for tracking the target vehicle, the speed curve for tracking the event target speed, the speed curve for tracking the maximum operating speed, and the speed curve for minimum risk maneuvering (MRM).

[0146] In the examples of this disclosure, the speed profile used to track the target speed of an event may include a speed profile used to track events including entering a curved road segment and entering a merging section, and the speed profile used to track the maximum operating speed may include a speed profile used to track the maximum operating speed, including road speed limits and the design maximum speed of the autonomous driving system.

[0147] An example of this disclosure provides an autonomous driving control method for an autonomous driving control device in a road merging section, the method comprising: selecting by the device; determining by the device at least one target candidate vehicle, the at least one target candidate vehicle being intended to enter the merging road in which the main vehicle is traveling in the road merging section; selecting by the device a final target vehicle from the at least one target candidate vehicle by using the target level of the at least one target candidate vehicle; and performing control by the device on the final target vehicle.

[0148] In the examples of this disclosure, selecting at least one target candidate vehicle may include: the device selecting at least one target candidate vehicle by using at least one of the following: the speed of the main vehicle, surrounding object information, merging section information, or a combination thereof.

[0149] In the examples of this disclosure, the surrounding object information may include the location or speed of objects around the main vehicle, and the merging section information may include at least one of the following: the starting point of the merging section, the ending point of the merging section, the type of the merging section, the direction of travel of the merging section, or a combination thereof.

[0150] In examples of this disclosure, selecting at least one target candidate vehicle may include: in response to the presence of a vehicle traveling in a merging section and the risk of collision with the main vehicle at the end of the merging section of the road exceeding a predetermined reference, selecting a vehicle as a target candidate vehicle by means of a device, the merging section being a lane adjacent to the lane in which the main vehicle is traveling.

[0151] In the examples of this disclosure, determining the target level of the at least one target candidate may include: the device determining the level by using at least one of a predetermined maximum level, a predetermined minimum level, the minimum time remaining until the predetermined target candidate vehicle reaches the merging endpoint, the maximum time remaining until the predetermined target candidate vehicle reaches the merging endpoint, and the remaining time until the target candidate vehicle reaches the merging endpoint, or a combination thereof.

[0152] In the examples of this disclosure, determining the target level of at least one target candidate may include determining the final level by the device using at least one of the following: level, predetermined maximum gain, predetermined minimum gain, predetermined maximum master vehicle speed, predetermined minimum master vehicle speed, master vehicle speed, or a combination thereof.

[0153] In the example disclosed herein, the final target vehicle is selected.

[0154] This can be included in the speed curves of target vehicles generated for tracking each of at least one target candidate vehicle, by the device selecting the target candidate vehicle with the speed curve having the minimum average acceleration over a predetermined time period as the final target candidate vehicle by comparing the average acceleration over a predetermined time period.

[0155] In an example of this disclosure, controlling a final target vehicle may include: generating a speed profile for tracking a target vehicle via a device; selecting the final target vehicle as the target vehicle, a speed profile for tracking an event target speed, a speed profile for tracking a maximum operating speed, and a speed profile for minimum risk maneuvering (MRM); comparing the average acceleration over a predetermined time in each of the speed profiles for tracking the target vehicle via the device, the target vehicle selecting the final target vehicle as the target vehicle, the speed profile for tracking an event target speed, the speed profile for tracking a maximum operating speed, and the speed profile for minimum risk maneuvering (MRM); and controlling the final target vehicle by tracking the speed profile with the minimum average acceleration over a predetermined time period.

[0156] According to this technology, by controlling an autonomous vehicle to drive at a level similar to that of an actual driver relative to another target merging into the road, the user satisfaction and reliability of the autonomous vehicle driving on the road in the road merging segment can be improved.

[0157] Furthermore, according to this technology, yield control can be effectively performed in road merging sections by selecting at least one yield candidate vehicle among surrounding objects during autonomous driving, determining the yield level of the at least one yield candidate vehicle, selecting the final yield vehicle, and performing yield control for the final yield vehicle.

[0158] The above description is merely an illustration of the technical concept of this disclosure, and those skilled in the art to which this disclosure pertains may make various modifications and changes without departing from the basic characteristics of this disclosure.

[0159] Therefore, the examples disclosed herein are not intended to limit the technical concept of this disclosure, but rather to illustrate it, and the scope of the technical concept of this disclosure is not limited by these examples. The scope of protection of this disclosure shall be interpreted as stated in the following claims, and all technical concepts within the equivalent scope shall be interpreted as included within the scope of this disclosure.

Claims

1. A device for controlling the automatic driving of a master vehicle, the device comprising: processor; as well as A memory storing at least one instruction, which, when executed by a processor communicating with the memory, is configured to cause the device to: Select at least one target candidate vehicle that is traveling in the merging section of the road and is expected to enter the road that the main vehicle is traveling on. A target yield level is determined associated with the at least one target candidate vehicle, wherein the target yield level corresponds to a value indicating the probability that the primary vehicle will yield to the at least one target candidate vehicle. Based on the determined target yield level, the final target vehicle is selected from the at least one target candidate vehicle. The signal is output based on the selected final target vehicle, and Based on the signal, the master vehicle is controlled to perform autonomous driving to give way to the final target vehicle.

2. The device according to claim 1, wherein, When executed by the processor communicating with the memory, the at least one instruction is configured to cause the device to select the final target vehicle from the at least one target candidate vehicles based on at least one of the following: the speed of the master vehicle, information about objects within a threshold distance of the master vehicle, and information about merging sections of the road.

3. The device according to claim 2, wherein, The information about the object includes the object's position or velocity.

4. The device according to claim 2, wherein, The merging section information includes at least one of the following: The starting point of the merging section of the road, The end point of the merging section of the road, The type of merging section of the road, and The direction of travel in the merging section of the road.

5. The device according to claim 1, wherein, When executed by the processor communicating with the memory, the at least one instruction is configured to cause the device to select a vehicle as a target candidate vehicle based on the following: The vehicle is traveling in a lane adjacent to the lane in which the main vehicle is traveling, and The vehicle has the risk of colliding with the main vehicle at the end of the merging section of the road, wherein the risk of collision exceeds a predetermined reference value.

6. The device according to claim 1, wherein, When the processor communicates with the memory, the at least one instruction is configured to cause the device to determine the target yield level associated with the at least one target candidate vehicle based on at least one of the following: The maximum clearance level is predetermined. Minimum yield level predetermined. The minimum time threshold for the at least one target candidate vehicle to reach the end of the merging section of the road. The maximum time threshold for the arrival of at least one target candidate vehicle at the end of the merging section of the road, and The current remaining time until the at least one target candidate vehicle reaches the end of the merging section of the road.

7. The device according to claim 1, wherein, When executed by the processor communicating with the memory, the at least one instruction is configured to cause the device to determine the final yield level based on at least one of the following: Target yield level, A predetermined maximum adjustment factor applied to the target yield level. A predetermined minimum adjustment factor applied to the target yield level. The predetermined maximum speed of the main vehicle, The predetermined minimum speed of the main vehicle, and The current speed of the main vehicle.

8. The device according to claim 7, wherein, When executed by the processor communicating with the memory, the at least one instruction is configured to cause the device to: For each of the at least one target candidate vehicle, a final yield level is determined, and Based on the following, a speed profile for tracking the target candidate vehicle is generated for each of the at least one target candidate vehicle: A target distance is controlled, wherein the target distance defines the inter-vehicle distance maintained between the master vehicle and the target candidate vehicle, and wherein the target distance is determined based on the final yield level. Deceleration adjustment parameters, wherein the deceleration adjustment parameters are used to adjust the deceleration rate of the master vehicle during tracking, and wherein the deceleration adjustment parameters are determined based on the final yield level.

9. The device according to claim 8, wherein, When executed by the processor communicating with the memory, the at least one instruction is configured to cause the device to select the target candidate vehicle with the lowest average acceleration over a predetermined time period as the final target vehicle by comparing the average acceleration over a predetermined time period in the speed curves generated for each of the at least one target candidate vehicle for tracking the target candidate vehicle.

10. The device according to claim 9, wherein, When executed by the processor communicating with the memory, the at least one instruction is configured to cause the device to generate, for the final target vehicle: Used to track the speed curve of the final target vehicle. A velocity curve used to track the speed of an event target. The speed curve used to track the maximum operating speed, and Speed ​​curves for minimum risk manipulation.

11. The device according to claim 10, wherein, The speed profile used to track the speed of the event target includes a speed profile generated for tracking in event conditions, wherein the event conditions include entering a curved road segment and a merging section entering the road, and The speed curve used to track the maximum operating speed includes a speed curve based on the maximum operating speed, wherein the maximum operating speed includes at least one of the road speed limit and the design maximum speed of the autonomous driving system.

12. A method performed by a device for controlling the autonomous driving of a master vehicle, the method comprising: Select at least one target candidate vehicle that is traveling in the merging section of the road and is expected to enter the road that the main vehicle is traveling on; Determine a target yield level associated with the at least one target candidate vehicle, wherein the target yield level corresponds to a value indicating the probability that the master vehicle will yield to the at least one target candidate vehicle; Based on the determined target yield level, the final target vehicle is selected from the at least one target candidate vehicle; The signal is output based on the selected final target vehicle; and Based on the signal, the master vehicle is controlled to perform autonomous driving to give way to the final target vehicle.

13. The method according to claim 12, wherein, The selection of the final target vehicle includes: selecting the final target vehicle from at least one of the at least one target candidate vehicles based on at least one of the speed of the main vehicle, information about objects within a threshold distance of the main vehicle, and information about merging sections of the road.

14. The method of claim 13, wherein: The information about the object includes the object's position or velocity, and The merging section information includes at least one of the following: The starting point of the merging section of the road, The end point of the merging section of the road, The type of merging section of the road, and The direction of travel in the merging section of the road.

15. The method according to claim 12, wherein, The selection of the final target vehicle includes selecting vehicles as target candidate vehicles based on the following criteria: The vehicle is traveling in a lane adjacent to the lane in which the main vehicle is traveling, and The vehicle has the risk of colliding with the main vehicle at the end of the merging section of the road, wherein the risk of collision exceeds a predetermined reference value.

16. The method according to claim 13, wherein, Determining the target yield level associated with the at least one target candidate vehicle includes determining the target yield level based on at least one of the following: The maximum clearance level is predetermined. Minimum yield level predetermined. The minimum time threshold for the at least one target candidate vehicle to reach the end of the merging section of the road. The maximum time threshold for the arrival of at least one target candidate vehicle at the end of the merging section of the road, and The current remaining time until the at least one target candidate vehicle reaches the end of the merging section of the road.

17. The method according to claim 16, wherein, The determination of the target yield level associated with the at least one target candidate vehicle further includes: determining the final yield level based on at least one of the following: The target yield level A predetermined maximum adjustment factor applied to the target yield level. A predetermined minimum adjustment factor applied to the target yield level. The predetermined maximum speed of the main vehicle, The predetermined minimum speed of the main vehicle, and The current speed of the main vehicle.

18. The method according to claim 17, wherein, The selection of the final target vehicle includes: by comparing the average acceleration over a predetermined time period in the speed curves generated for each of the at least one target candidate vehicle to track the target candidate vehicle, selecting the target candidate vehicle with the speed curve having the minimum average acceleration over the predetermined time period as the final target vehicle.

19. An apparatus for controlling the automatic driving of a vehicle, the apparatus comprising: processor; as well as A memory storing at least one instruction, which, when executed by a processor communicating with the memory, is configured to cause the device to: Based on at least one of object information associated with the vehicle and merging section information associated with the merging road, at least one target candidate vehicle is selected that is expected to enter the merging road that the vehicle is traveling on. Based on at least one of collision risk and the remaining time until the at least one target candidate vehicle reaches the end of the merging road, a yield level associated with each of the at least one target candidate vehicle is determined, wherein the yield level corresponds to a value indicating the probability that the vehicle yields to the at least one target candidate vehicle; Based on the determined yield level, a target vehicle is selected from the at least one target candidate vehicle; Output a signal indicating the selected target vehicle; and Based on the signal, the vehicle is controlled to drive autonomously to give way to the selected target vehicle.

20. The apparatus according to claim 19, wherein: The object information includes at least one of the object's position and speed within the threshold range of the vehicle; The merging section information includes at least one of the following: the starting point, the ending point, the type, and the direction of travel of the merging road; The selected target vehicle has a speed curve in which the average acceleration over a predetermined time period is minimized; and The yield level is also determined based on at least one of a predetermined maximum yield level and a predetermined minimum yield level.

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