Vehicle control device and method

Abstract

The present application relates to vehicle control apparatus and method. A vehicle control apparatus for a vehicle is provided. The vehicle control apparatus can include a display apparatus, one or more sensors configured to obtain vehicle travel information about the vehicle and external object information about an external object, one or more processors, and a memory storing at least one instruction. The at least one instruction can be configured to cause the vehicle control apparatus to determine, based on the vehicle travel information and the external object information, that a probability score indicative of a likelihood of the external object interfering with a travel path of the vehicle is above a threshold value, determine, based on the probability score being above the threshold value, a vehicle control strategy for the vehicle, and display, by the display apparatus and based on the vehicle control strategy, a planned autonomous driving path for the vehicle.

Description

[0001] Cross-reference to related applications This application claims priority and benefit to Korean Patent Application No. 10-2025-0017121, filed on February 11, 2025, with the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This invention relates to vehicle control devices and methods. Background Technology

[0003] As autonomous vehicle technology advances, the technology that enables vehicles to plan their own routes and handle interactions with nearby objects while driving becomes increasingly important. Autonomous vehicles can display their routes and objects of interest (e.g., objects to follow or avoid) on a map to visually communicate the intentions of the autonomous driving system to the driver. While autonomous driving systems can provide information about, for example, the vehicle's current route and / or specific objects of interest, one limitation of such systems may be that the information provided to the driver may not be intuitive or sufficient to convey how the autonomous driving logic appropriately plans the route based on the surrounding environment.

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

[0005] The aim of this invention is to provide a vehicle control device and method that can intuitively display the control strategy of an autonomous vehicle.

[0006] The present invention also aims to provide a vehicle control device and method that can determine the vehicle's driving strategy by taking into account interference from external objects and visually display the determined driving strategy.

[0007] The present invention also aims to provide a vehicle control device and method that can update and display the vehicle's driving strategy in real time when the driving strategy changes.

[0008] According to one or more exemplary embodiments of the present invention, a vehicle control device for a vehicle may include: a display device; one or more sensors configured to acquire vehicle driving information about the vehicle and external object information about external objects; one or more processors; and a memory storing at least one instruction. The at least one instruction may be configured, when executed by one or more processors communicating with the memory, to cause the vehicle control device to: determine, based on the vehicle driving information and the external object information, that a probability score indicating the likelihood of interference between an external object and the vehicle's driving path is higher than a threshold; determine a vehicle control strategy based on the probability score being higher than the threshold; and display a planned autonomous driving path for the vehicle via the display device and based on the vehicle control strategy.

[0009] The external object information may include motion information about the external object. The at least one instruction may be configured, when executed by one or more processors communicating with the memory, to further cause the vehicle control device to: determine the expected motion path of the external object based on the object type and the motion information about the external object.

[0010] The at least one instruction may be configured to, when executed by one or more processors communicating with the memory, further enable the vehicle control device to determine a probability score based on the predicted motion path of an external object and vehicle driving information.

[0011] The at least one instruction may be configured, when executed by one or more processors communicating with the memory, to cause the vehicle control device to display the vehicle's planned autonomous driving path in such a way as to determine at least one of the area, color, contrast, brightness, shape, or chromaticity of the planned autonomous driving path displayed on the display device based on the expected motion path of external objects and the vehicle control strategy.

[0012] The at least one instruction may be configured to, when executed by one or more processors communicating with the memory, cause the vehicle to display the vehicle's planned autonomous driving path in the following manner: adjusting the expected motion path of the external object based on updated external object information; modifying the vehicle control strategy based on the adjusted expected motion path of the external object; and adjusting at least one of the area, color, contrast, brightness, shape, or chromaticity of the planned autonomous driving path displayed through the display device based on the modified vehicle control strategy.

[0013] The at least one instruction can be configured, when executed by one or more processors communicating with the memory, to cause the vehicle to determine its vehicle control strategy by further determining the vehicle control strategy based on the expected motion path of external objects and vehicle driving information.

[0014] The at least one instruction may be configured to, when executed by one or more processors communicating with the memory, cause the vehicle to determine a vehicle control strategy by determining at least one of the following: whether to perform lateral control of the vehicle, whether to overtake an external object, or whether to follow an external object.

[0015] The at least one instruction may be configured, when executed by one or more processors communicating with memory, to cause the vehicle to determine a vehicle control strategy in such a way as to determine at least one of the following: a first vehicle control strategy including following an external object without performing lateral control of the vehicle; a second vehicle control strategy including overtaking an external object without performing lateral control of the vehicle; a third vehicle control strategy including following an external object and performing lateral control of the vehicle; or a fourth vehicle control strategy including overtaking an external object and performing lateral control of the vehicle.

[0016] The at least one instruction may be configured to, when executed by one or more processors communicating with the memory, cause the vehicle to determine a vehicle control strategy by adjusting the vehicle control strategy based on at least one of road speed limits or traffic regulations.

[0017] The at least one instruction can be configured to, when executed by one or more processors communicating with the memory, cause the vehicle to determine a vehicle control strategy by adjusting the vehicle control strategy based on the driving purpose associated with the vehicle.

[0018] According to one or more exemplary embodiments of the present invention, a method performed by a vehicle device may include: acquiring vehicle driving information about the vehicle and external object information about external objects through one or more sensors of the vehicle; determining, based on the vehicle driving information and the external object information, that a probability score indicating the possibility of interference between the external object and the vehicle's driving path is higher than a threshold; determining a vehicle control strategy for the vehicle based on the probability score being higher than the threshold; and displaying the planned autonomous driving path of the vehicle through a display device and based on the vehicle control strategy.

[0019] The external object information may include motion information about the external object. The method may further include: determining the expected motion path of the external object based on its object type and the motion information.

[0020] The method may further include: determining a probability score based on the predicted motion path of the external object and the vehicle's driving information.

[0021] Displaying the planned autonomous driving path of the vehicle may include: determining at least one of the area, color, contrast, brightness, shape, or chromaticity of the planned autonomous driving path displayed on the display device based on the expected motion path of external objects and the vehicle control strategy.

[0022] Displaying the planned autonomous driving path of the vehicle may include: adjusting the expected motion path of the external object based on updated external object information; modifying the vehicle control strategy based on the adjusted expected motion path of the external object; and adjusting at least one of the area, color, contrast, brightness, shape, or chromaticity of the planned autonomous driving path displayed through the display device based on the modified vehicle control strategy.

[0023] Determining a vehicle control strategy may include further determining the vehicle control strategy based on the predicted motion path of external objects and vehicle driving information.

[0024] Determining a vehicle control strategy may include determining at least one of the following: whether to perform lateral control of the vehicle, whether to overtake an external object, or whether to follow an external object.

[0025] Determining a vehicle control strategy may include determining at least one of the following: a first vehicle control strategy that includes following an external object without performing lateral control of the vehicle; a second vehicle control strategy that includes overtaking an external object without performing lateral control of the vehicle; a third vehicle control strategy that includes following an external object and performing lateral control of the vehicle; or a fourth vehicle control strategy that includes overtaking an external object and performing lateral control of the vehicle.

[0026] Determining a vehicle's control strategy may include adjusting the vehicle control strategy based on at least one of road speed limits or traffic regulations.

[0027] Determining a vehicle's control strategy may include adjusting the vehicle control strategy based on the driving purpose associated with the vehicle. Attached Figure Description

[0028] The objects, features, and advantages of the invention will become more apparent to those skilled in the art from a detailed description of one or more exemplary embodiments of the invention with reference to the accompanying drawings, in which: Figure 1 This is a diagram illustrating an example vehicle that sends and receives data by communicating with other devices; Figure 2 This is a block diagram illustrating the components of an example vehicle; Figure 3 This is a flowchart illustrating the operation of an example vehicle control device; Figure 4 , Figure 5, Figure 6 , Figure 7 This is a diagram illustrating example operations of the processor; and Figure 8 This is a flowchart of an example method for controlling a vehicle. Detailed Implementation

[0029] In the following, one or more exemplary embodiments of the invention will be described in detail with reference to the accompanying drawings.

[0030] However, the technical concept of the present invention is not limited to the certain example embodiments described, but can be implemented in various different forms. Within the scope of the technical concept of the present invention, one or more components of the example embodiments can be used by selective combination and substitution.

[0031] Furthermore, unless specifically defined and described, the terms (including technical and scientific terms) used in the exemplary embodiments of the present invention may be interpreted as meaning commonly understood by one of ordinary skill in the art to which this invention pertains, and common terms such as those defined in dictionaries may be interpreted taking into account the contextual meaning of the relevant art.

[0032] The terminology used in this invention is for the purpose of describing exemplary embodiments only and is not intended to limit the invention.

[0033] In this specification, unless the context clearly specifies otherwise, the singular form may include the plural form, and when described as “at least one (or one or more) of A, B and / or C,” it may include one or more of all possible combinations of A, B, and C. For the purposes of this application and claims, the exemplary phrase “at least one of the following: A, B, or C” or “at least one of A, B, or C” is used, which means “at least one A, or at least one B, or at least one C, or any combination of at least one A, at least one B, and at least one C.” Furthermore, 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., as used herein, may represent each enumerated item or all possible combinations of enumerated 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.

[0034] Furthermore, when describing components of exemplary embodiments of the present invention, terms such as first, second, A, B, (a), (b), etc., may be used.

[0035] These terms are used only to distinguish components from other components; the nature, order, or sequence of components are not limited by these terms.

[0036] Furthermore, when a component is described as “linked,” “joined,” or “connected” to another component, the component is not only directly linked, joined, or connected to the other component, but also “linked,” “joined,” or “connected” to the other component when yet another component is arranged between the component and the other component.

[0037] Furthermore, when a component is described as being formed on or arranged "above" or "below" another component, the term "above" or "below" includes not only cases where the two components are in direct contact with each other, but also cases where one or more other components are formed on or arranged between the two components. Additionally, when a component is described as being "above" or "below," the description can include meanings based on the upward and downward directions of a component.

[0038] According to the Society of Automotive Engineers (SAE), the automation levels of autonomous vehicles can be classified as follows: Level 0, corresponding to "No Automation," involves the autonomous driving system temporarily engaging in emergency situations (e.g., automatic emergency braking) and / or only providing warnings (e.g., blind spot warning, lane departure warning, etc.), and the driver must operate the vehicle. Level 1, corresponding to "Driver Assistance," involves the system performing some driving functions (e.g., steering, acceleration, braking, lane centering, adaptive cruise control, etc.), while the driver operates the vehicle within the normal operating range, and the driver must determine the system's operating status and / or timing, perform other driving functions, and respond to (e.g., resolve) emergency situations. Level 2, corresponding to "Partial Automation," involves the system performing steering, acceleration, and / or braking under driver supervision, where the driver must determine the system's operating status and / or timing, perform other driving functions, and respond to (e.g., resolve) emergency situations. At Level 3 of autonomous 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 required conditions are not met. The driver needs to determine the system's operating state and / or timing and take over control in an emergency, but does not otherwise operate the vehicle (e.g., steering, acceleration, and / or braking). At Level 4 of autonomous driving, the SAE classification standard can correspond to "high automation," where the system performs all driving functions, and the driver only needs to take over control of the vehicle in an emergency. At Level 5 of autonomous driving, the SAE classification standard can correspond to "full automation," where the system performs all driving functions without any driver assistance (including in an emergency), and the driver does not need to perform any driving functions other than determining the system's operating state. Although the present invention can apply the SAE classification standard to autonomous driving classification, other classification methods and / or algorithms can be used in one or more configurations described herein. One or more features associated with autonomous driving control can be activated based on the configured autonomous driving control settings (e.g., based on at least one of the following: autonomous driving classification, selection of the vehicle's autonomous driving level, etc.).

[0039] Based on one or more features described herein (e.g., predicting the trajectory of an object), vehicle operation can be controlled. Vehicle control can include various operational controls associated with the vehicle (e.g., autonomous driving control, sensor control, braking control, braking timing control, acceleration control, rate of change of acceleration control, warning timing control, forward collision warning timing control, etc.).

[0040] For example, one or more auxiliary devices (e.g., engine brakes, exhaust brakes, hydraulic retarders, electric retarders, regenerative brakes, etc.) can also be controlled based on one or more features described herein (e.g., predicting the trajectory of an object). Similarly, 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 (e.g., 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 based on one or more features described herein (e.g., predicting the trajectory of an object).

[0041] For example, minimum risk policy (MRM) operations can also be controlled based on one or more features described herein (e.g., predicting the trajectory of an object). Minimum risk policy operations (e.g., minimum risk policy, minimum risk dispatch) can be policy operations that minimize (e.g., reduce) the risk of collisions with surrounding vehicles to achieve a reduced (e.g., minimum) risk state. Minimum risk policies can be operations that can be activated during autonomous driving when the driver is unable to respond to intervention requests. During a minimum risk policy, one or more processors in the vehicle can control the vehicle's driving operations for a set time period.

[0042] For example, biased driving operations can also be controlled based on one or more features described herein (e.g., predicting the trajectory of an object). The driving control unit can perform biased driving control. To perform biased driving, the driving control unit can control the vehicle to stay within the lane by maintaining a lateral distance between the vehicle's center position and the lane center. For example, the driving control unit can control the vehicle to remain within the lane, but not in the center of the lane.

[0043] The driving control unit can identify a biased target lateral distance for biased driving control. For example, the biased target lateral distance can include an intentionally adjusted lateral distance that the vehicle may aim to maintain relative to a reference point (e.g., lane center or another vehicle) during strategies such as lane changes. This adjustment can be made to improve the vehicle's stability, safety, and / or performance under different driving conditions. For instance, during a lane change, the driving control system may bias the lateral distance to maintain a safer distance from adjacent vehicles, taking into account factors such as vehicle speed, road conditions, and / or the presence of obstacles.

[0044] For example, the level of autonomous driving and / or activation / deactivation of autonomous driving can also be controlled based on one or more features described herein (e.g., predicting the trajectory of an object). The driving control unit can perform autonomous driving level control (e.g., changing the level of autonomous driving, changing the required user attention, etc.) or deactivate autonomous driving operation. For example, by changing the required user attention, the driver may need to place his / her hands on the drive wheels more frequently (e.g., at least once within a threshold time period (e.g., 5 seconds, 30 seconds, 1 minute, etc.)). By changing the required user attention, the driver may need to look forward more frequently (e.g., at least once within a threshold time period (e.g., 5 seconds, 30 seconds, 1 minute, etc.)). By changing the level of autonomous driving, one or more video contents may not be displayed on the vehicle's display.

[0045] For example, one or more sensors (e.g., IMU sensors, camera devices, 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 based on one or more features described herein (e.g., predicting the trajectory of an object).

[0046] Operational control for autonomous driving of a vehicle may include various driving controls of the vehicle by vehicle control units (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, driver warning control, automated driving operation design domain (ODD), enabling and / or disabling automated driving modes, etc.). For example, operational control may further include displaying a predicted trajectory of an object (e.g., a target vehicle) via a user interface (e.g., a display device) to notify the vehicle occupants (e.g., the driver).

[0047] The vehicle actively controlled by the autonomous driving system can be referred to as the ego vehicle, host vehicle, or autonomous vehicle. The host vehicle can also be referred to as a self-driving car, autonomous vehicle (AC), driverless car, robotaxi, robotic car, or robo-car. The host vehicle can be a vehicle equipped with an autonomous driving system. Alternatively, the autonomous driving system can control the host vehicle, for example, from an external and / or remote device (e.g., a server). The host vehicle can be remotely partially or fully controlled by a remote human driver (e.g., piloted, driving, etc.). A vehicle located in front of the host vehicle (e.g., in the same lane as the host vehicle) can be referred to as the preceding vehicle (e.g., the vehicle directly in front), the leading vehicle, the lead vehicle, or the preceding vehicle. A vehicle following the host vehicle (e.g., in the same lane as the host vehicle) can be referred to as the following vehicle, the trailing vehicle, the following vehicle, or the rear vehicle. Adjacent vehicles can refer to any vehicle located in any direction (e.g., in front, behind, to the left, to the right, diagonally, etc.) of the vehicle, provided that there are no other vehicles (e.g., intermediate vehicles) between the vehicle and the vehicle (e.g., regardless of their distance from the vehicle). Alternatively, in some cases, only those vehicles located within a threshold distance of the vehicle (e.g., the line-of-sight and / or detection limit of one or more sensors of the vehicle) can be called adjacent vehicles. Target vehicles can be any vehicle near the vehicle (e.g., within a threshold distance of the vehicle). Target vehicles can be any vehicle actively or passively monitored, identified, confirmed, tracked, and / or analyzed by the autonomous driving system, once or multiple times, occasionally or continuously. For example, the threshold distance can be the line-of-sight and / or detection limit of one or more sensors of the vehicle, but the threshold distance can be a value smaller than the line-of-sight and / or detection limit of one or more sensors of the vehicle (e.g., an adjustable value). For example, a target vehicle can be a vehicle in front, a vehicle behind, a vehicle in a lane different from the vehicle's lane (e.g., a vehicle on the left, a vehicle on the right, a vehicle diagonally opposite, etc.), and / or an adjacent vehicle (e.g., regardless of its distance from the vehicle and / or regardless of whether there is a vehicle between the target vehicle and the vehicle). A target vehicle can also be referred to as a surrounding vehicle, a nearby vehicle, an external vehicle, another vehicle (other vehicles), etc.

[0048] At least in some implementations of autonomous driving systems, when an autonomous vehicle needs to avoid a specific object, there can be various options during the avoidance process. For example, the vehicle's acceleration or deceleration mode can change depending on whether the vehicle decides to avoid the object while passing it or to follow it after avoiding it.

[0049] However, simply displaying the object to be avoided and the driving path may present a problem: the driver may find it difficult to intuitively understand the avoidance method chosen by the autonomous driving logic.

[0050] Such limitations may prevent the decision-making process of autonomous vehicles from being clearly communicated to the driver. Consequently, the reliability of autonomous driving systems (e.g., the reliability perceived by the driver) may be reduced, and may lead to driver anxiety.

[0051] Therefore, a technology is needed to more clearly and intuitively visualize and communicate to the driver the surrounding environmental factors considered by the autonomous driving logic, as well as the resulting path planning and selection process.

[0052] In the following description, one or more exemplary embodiments will be described in detail with reference to the accompanying drawings, and regardless of the drawing numbers, the same or corresponding components will be indicated by the same reference numerals, and redundant descriptions will be omitted.

[0053] In the following text, reference will be made to Figure 1 and Figure 2 Describe the vehicle. Figure 1 This is a diagram illustrating an example vehicle that sends and receives data by communicating with other devices.

[0054] refer to Figure 1 Vehicle 100 can be driven by either electricity or fossil fuels. In the case of an electric vehicle, vehicle 100 can be, for example, a pure battery-based vehicle powered solely by a high-voltage battery, or it can utilize a gas-based fuel cell as its energy source. Furthermore, the fuel cell can utilize various types of gases capable of generating electricity, and vehicle 100 can be filled with, for example, a liquefied gas. An example of such a gas is hydrogen. However, the gas is not limited to this; various gases are applicable. In the case of a fossil fuel-based vehicle, vehicle 100 is driven by fuel (e.g., gasoline, diesel, or liquefied petroleum gas) and can be equipped with an internal combustion engine that drives the actuation unit 116 through the combustion of fuel. The engine can be included in the energy generation unit 110 to provide the driving rotational force to the wheel drive unit 118. As another example, vehicle 100 can selectively utilize energy from a fossil fuel-based internal combustion engine and energy from a battery to drive the actuation unit 116, and can be a hybrid vehicle.

[0055] Vehicle 100 can refer to a mobile device. Vehicle 100 is a ground vehicle that travels on the ground and can be a typical passenger car, commercial vehicle, purpose-built vehicle (PBV), etc. Vehicle 100 can be a four-wheeled vehicle (e.g., a passenger car, sport utility vehicle (SUV), or mini-truck) or a vehicle with more than four wheels (e.g., a bus, large truck, container truck, heavy equipment vehicle, etc.). Here, ground vehicle can refer to any vehicle including vehicles that move underground and vehicles that move on the ground. Vehicle 100 can be a robot in the broad sense, such as a mobile device, and the robot can move using wheels, tracks, or other mobility modules. In this invention, ground mobile devices such as ground vehicles are primarily described, but unless contradicted by this invention, exemplary embodiments can also be applied to air mobile devices such as advanced air mobility (AAM), aircraft, etc., and water mobile devices such as ships, submarines, etc.

[0056] Vehicle 100 can be controlled and driven via autonomous driving, which can be either semi-autonomous or fully autonomous. Fully autonomous driving provides automatic movement where, even in uncertain driving conditions, the processor 130 of vehicle 100 maintains full control without user intervention. Semi-autonomous driving provides automatic movement requiring driver intervention depending on the specific driving conditions. Semi-autonomous driving can be implemented such that, in the event of such conditions, the processor 130 deactivates autonomous driving to transfer control to the user, enabling the user to perform manual driving. According to the levels of autonomous driving defined by the Society of Automotive Engineers (SAE), semi-autonomous driving can correspond to levels 1 through 4, and fully autonomous driving can correspond to level 5.

[0057] Simultaneously, vehicle 100 can communicate with other devices 200 and 300 or another vehicle 400. For example, other devices may include server 200, intelligent transportation system (ITS) device 300, various types of user devices, etc., where server 200 supports various controls, status management, and driving functions of vehicle 100; and intelligent transportation system device 300 is used to receive information from the ITS. For example, server 200 may be an external device operated by the vehicle manufacturer or configured to serve autonomous driving, and can receive connection data from vehicle 100 or send data required for autonomous driving. In response to requests and data sent from vehicle 100 and user devices, server 200 can send various information and software modules for controlling vehicle 100 to support autonomous driving and various services of vehicle 100.

[0058] For example, ITS device 300 may be a roadside unit (RSU), and ITS device 300 may assist the user in driving his or her vehicle or support the autonomous driving of vehicle 100 by exchanging vehicle identification data, driving control and status data, environmental data around the vehicle, map data, etc. with vehicle 100 via vehicle-to-infrastructure (V2I) communication. Vehicle 100 may support manual or autonomous driving by exchanging the data listed herein with other vehicles 400 via vehicle-to-vehicle (V2V) communication.

[0059] Vehicle 100 can communicate with other vehicles or other devices based on cellular communication, wireless access invehicular environment (WAVE) communication, dedicated short range communication (DSRC), short-range communication or other communication methods.

[0060] For example, vehicle 100 can use cellular communication networks such as LTE or 5G, Wi-Fi communication networks, or WAVE communication networks to communicate with server 200, ITS device 300, and other vehicles 400. As another example, DSRC or similar technologies used in vehicle 100 can be used for communication between vehicles. The communication methods between vehicle 100, server 200, ITS device 300, other vehicles 400, and user equipment are not limited to the example embodiments described herein.

[0061] Figure 2 This is a block diagram showing the components of an example vehicle.

[0062] Vehicle 100 may include a sensor unit (also referred to as one or more sensors) 102, an operation unit (also referred to as a user interface or input and output device) 106, a display device 108, a load device (also referred to as an electrical load) 114, and a transmit / receive unit (also referred to as a communication interface) 112.

[0063] The sensor unit 102 may be equipped with various types of detectors to detect various states and situations occurring in the external environment, internal systems, user operations, and vehicle space of the vehicle 100.

[0064] Specifically, the first sensor unit 102 may be equipped with an externally facing camera device 102a, a lidar sensor 102b, a radar sensor 102c, etc., to identify dynamic and static objects existing outside the vehicle 100. The camera device 102a can identify external objects as images when the vehicle 100 is in use, generate image data, and send the image data to the processor 130. The lidar sensor 102b can generate point cloud data as data for identifying external objects and send the point cloud data to the processor 130 to generate 3D spatial information that at least identifies the shape of the external object. To determine the presence of the external object and its relative distance, speed, direction, etc., the radar sensor 102c can emit radio waves of a specific frequency around the vehicle 100 and generate radar data from the radio waves reflected from the external object. In this invention, the sensor unit is shown with a lidar sensor 102b, but in other examples, the lidar sensor 102b may not be installed.

[0065] The first sensor unit 102 can generate object recognition information based on sensing data. The object recognition information may include information about the existence of the object, information about the object's position, information about the distance between the vehicle 100 and the object, and information about the relative speed between the vehicle 100 and the object. The external object can be any object related to the operation of the vehicle 100.

[0066] The second sensor unit 103 may be equipped with a position sensor 103a, a wheel sensor 103b, an attitude sensor 103c, etc., to confirm its own position, speed, driving attitude, etc. The attitude sensor 103c may include a gyroscope sensor, an angular velocity sensor, an acceleration sensor, etc. The attitude sensor may be an inertial measurement unit (IMU) sensor and may be equipped with a 3-axis accelerometer and a 3-axis gyroscope. The attitude sensor 103c can measure the acceleration of the vehicle 100 in the driving direction (x), the acceleration in the lateral direction (y), and the acceleration in the height direction (z), as well as yaw, pitch, and roll as the angular velocity of the vehicle.

[0067] The second sensor unit 103 can generate vehicle driving information based on the sensing data. The vehicle driving information can be generated based on data detected by various sensors installed within the vehicle. For example, vehicle driving information may include vehicle attitude information, vehicle speed information, vehicle tilt information, vehicle weight information, vehicle direction information, vehicle battery information, vehicle fuel information, vehicle tire pressure information, vehicle steering information, vehicle interior temperature information, vehicle interior humidity information, pedal position information, vehicle engine temperature information, etc.

[0068] In addition, vehicle driving information may include route information. Route information may refer to information generated based on the destination input by the vehicle user through operation unit 106. When a destination has been set, route information may refer to information on a map indicating the driving route from the vehicle's current location to the destination. When no destination has been set, route information may refer to information including the road the vehicle is currently traveling on and the future driving route including that road.

[0069] Operating unit 106 can be configured as a module controlled by a user for driving. Operating unit 106 can be a user interface, which refers to any device through which a human user (e.g., a driver) can interact with a device (e.g., a vehicle). Operating unit 106 may therefore include input interfaces (also called input devices) and / or output interfaces (also called output devices), the input interfaces receiving input from a human user and data or information being output to the human user via the output interfaces. For example, input interfaces may include buttons, knobs, toggle switches, switches, dials, sliders, keyboards, touchscreens, control panels, dashboards, center consoles (also called center consoles), microphones, levers (e.g., control levers), shift controls (e.g., shift levers, shift paddles, etc.), camera devices, wheels, steering wheels, pedals, joysticks, etc. For example, output interfaces may include lights (e.g., indicator lights), illuminators, indicators, screens, display devices, consoles, dashboards, meters, gauges, speakers, etc. Any input interface described herein may also be an output interface, and vice versa. For example, the central console can be considered both an input interface (e.g., equipped with buttons, knobs, sliders, touchscreens, etc.) and an output interface (e.g., equipped with displays, indicator lights, etc.). For example, the operating unit 106 can be a steering wheel for manual driving, an automatic or manual transmission, an accelerator pedal, a brake pedal, etc. The operating unit 106 can be further provided with interfaces for enabling or disabling the autonomous driving mode and selecting detailed functions requested by the user, allowing the user to use the autonomous driving function. To receive various requests related to autonomous driving, the operating unit 106 can be configured, for example, as a hard interface located at a predetermined location within the vehicle 100 or as a soft interface that can be touched on the display device 108. Depending on the specifications of the autonomous vehicle, at least one of the steering wheel, transmission, and pedals can be omitted. As another example, the operating unit 106 can be provided with a module that receives user control requests for the load device 114 in addition to driving control.

[0070] Display device 108 can be used as a user interface. Display device 108 can output and display information such as the operating status and control status of vehicle 100, route / traffic information, remaining energy level, and driver requests via processor 130. Furthermore, display device 108 can be configured as a touchscreen capable of detecting driver input to receive requests from the driver instructing processor 130.

[0071] The load device 114 is mounted on the vehicle 100 and can be a non-drive electrical device that does not include a drive power system (e.g., wheel drive unit 118, etc.). The load device 114 is an auxiliary device that receives electricity from the energy generation unit 110 and can be, for example, an air conditioning system, a lighting system, a seating system, various devices installed in the vehicle 100, etc. In this invention, a cooling / heating system may be further included to cool or heat at least one of the battery, fuel cell, internal combustion engine, air conditioning system, and specific components of the vehicle 100.

[0072] The transmitting / receiving unit 112 can support communication with the server 200, the ITS device 300, nearby vehicles 400, etc. The transmitting / receiving unit 112 may include modules for processing, for example, cellular communication, WAVE, DSRC communication, etc. In this invention, the transmitting / receiving unit 112 can transmit data generated or stored during driving to the server 200 and receive data and software modules transmitted from the server 200. The transmitting / receiving unit 112 can support communication with electronic devices carried by occupants within the vehicle 100. In this invention, the vehicle 100 can transmit and receive data used in the method according to the invention from the outside via the transmitting / receiving unit 112.

[0073] For example, the transmitting / receiving unit 112 can receive traffic signal information from the traffic signal controller and provide the traffic signal information to the processor 130. In addition, the transmitting / receiving unit 112 can receive control signals from the traffic signal controller and provide the control signals to the processor 130.

[0074] In addition, the vehicle 100 may include an energy generation unit 110 and an actuation unit 116.

[0075] Energy generation unit 110 can generate and supply power and electricity used in driving and non-driving power systems, such as actuation unit 116. For example, the non-driving power system may be, but is not limited to, sensor unit 102, operation unit 106, display device 108, load device 114, and transmit / receive unit 112, and may include various components for sensing, interface, communication, and convenience functions, excluding components directly involved in driving operations. When vehicle 100 is driven by electric energy, energy generation unit 110 can be configured as an externally charged battery, or as a combination of a battery and a fuel cell for charging the battery. In the case of a vehicle driven by a combination of battery and fuel cell, energy generation unit 110 may include a tank for storing materials (e.g., liquefied hydrogen) used to generate electricity from the fuel cell. When vehicle 100 is driven by fossil fuels, energy generation unit 110 can be configured as an internal combustion engine. Furthermore, when vehicle 100 is a hybrid vehicle (e.g., a hybrid electric vehicle), energy generation unit 110 can be configured as a combination of an internal combustion engine and a battery.

[0076] Actuation unit 116 may be provided with at least one module for implementing driving operations and perform at least one driving operation among longitudinal control (e.g., acceleration and deceleration) and lateral control (e.g., steering) according to user requests from operation unit 106. To execute driving operations according to commands from processor 130 via manual operation or automatic driving by the user, actuation unit 116 may be provided with wheel drive unit 118 and mechanical components and electronic modules for implementing drive operations in wheel drive unit 118. When vehicle 100 operates on electric power, actuation unit 116 may include components for sending requested driving operations to wheel drive unit 118. When vehicle 100 operates on fossil fuel power, actuation unit 116 may be provided with a transmission and gear module for transmitting power from an internal combustion engine.

[0077] The wheel drive unit 118 may include multiple wheels, a drive force generating module for generating and applying or transmitting drive force to the wheels, a braking module for decelerating the drive of the wheels, and a steering module for achieving lateral control of the wheels. When the vehicle 100 is driven by electric power, the drive force generating module may be configured as a motor assembly that generates drive force based on electricity output from a battery. The braking module of the electric vehicle 100 may further have regenerative braking functionality.

[0078] The navigation system 122 can provide navigation information. The navigation information may include at least one of the following: map information, set destination information, route information based on the set destination, information about various objects on the route, lane information, and current vehicle position information.

[0079] The navigation system 122 can receive information from external devices and update previously stored information through the transmit / receive unit 112. The navigation system 122 can be classified as a sub-component of the operation unit 106.

[0080] In addition, vehicle 100 may include memory 120 and processor 130.

[0081] The memory 120 can store applications and various types of data used to control the vehicle 100, and can load applications or read and record data upon request from the processor 130.

[0082] Processor 130 can perform overall control of vehicle 100. Processor 130 can be configured to execute applications and instructions stored in memory 120.

[0083] Figure 3 This is a flowchart illustrating the operation of an example vehicle control unit. (Reference) Figure 3 The vehicle control unit 10 may include a display device (also called a display equipment) 11, a sensor unit (also called one or more sensors) 12, and a processor 13. Figure 3 The display device 11 and processor 13 in the middle can each have the same as Figure 2 The display device and processor have the same configuration. Figure 3 The sensor unit 12 in the text may refer to including Figure 2 The components of the first sensor unit and the second sensor unit in the process.

[0084] The sensor unit 12 can collect vehicle driving information and external object recognition information (also known as external object information) about the vehicle.

[0085] As described herein, external object recognition information may include information about the object's presence, its location, the distance between the vehicle and the object, and their relative speed. External objects can be various objects relevant to the operation of the vehicle.

[0086] In addition, vehicle driving information for the main vehicle may include vehicle attitude information, vehicle speed information, vehicle tilt information, vehicle weight information, vehicle direction information, vehicle battery information, vehicle fuel information, vehicle tire pressure information, vehicle steering information, vehicle interior temperature information, vehicle interior humidity information, pedal position information, vehicle engine temperature information, etc.

[0087] In addition, vehicle driving information may include route information. Route information may refer to information generated based on the destination input by the vehicle user through the operating unit. When a destination has been set, route information may refer to information on the map indicating the driving route from the vehicle's current location to the destination. When no destination has been set, route information may refer to information including the road the vehicle is currently traveling on and the future driving route including that road.

[0088] The processor 13 can determine, based on vehicle driving information and external object recognition information, external objects that are expected to interfere with the vehicle's driving path (e.g., intersecting the vehicle's driving path or within a threshold distance of the vehicle's driving path). In other words, the processor 13 can determine that the probability score indicating the likelihood of an external object interfering with the vehicle's driving path is higher than a threshold.

[0089] Processor 13 can determine the expected path of an external object based on the type of the external object (e.g., object type or classification, such as car, sedan, SUV, compact vehicle, truck, bus, motorcycle, bicycle, pedestrian, etc.) and motion information about the external object included in the external object identification information (e.g., the expected motion path of the object). For example, the expected path of the external object can be determined based on a predetermined (e.g., typical or known) motion pattern associated with a particular object type. For example, if the external object is identified as a motorcycle, the expected path of the external object can be determined based on the known motion patterns of a typical motorcycle.

[0090] In this way, the processor 13 can determine the external objects that are expected to interfere based on the expected path of the external objects and the vehicle's driving information.

[0091] The processor 13 can estimate the current position by tracking the past position of an external object based on external object recognition information. The processor 13 can continuously observe the current position, speed, direction, etc. of a moving external object and track its changes over time.

[0092] For example, processor 13 can predict future positions based on state information about external objects, using a Kalman filter, an extended Kalman filter, or a particle filter.

[0093] Processor 13 can predict the most likely path of an external object, such as a vehicle or motorcycle, based on lane information about the road on which the vehicle is traveling. Processor 13 can perform path prediction by taking into account the current speed and direction of the external object, as well as the curvature and continuity of the lane.

[0094] Processor 13 can model the behavior of objects not constrained by lanes (e.g., pedestrians or animals) based on external object recognition information. For example, pedestrians tend to follow road rules (e.g., crosswalks, sidewalks), and animals are more likely to exhibit random movement. Processor 13 can model the behavior of external objects by applying a trained model based on past data (e.g., deep learning) or a rule-based system.

[0095] The processor 13 can determine, in a probabilistic manner, multiple paths that an external object may take under various conditions. For example, the processor 13 can generate multiple hypothetical paths to represent the uncertainty of future paths and assign a probability value to each path.

[0096] The processor 13 can classify external objects into moving objects (e.g., vehicles, pedestrians, or animals) and static objects (e.g., cones or guardrails) based on external object recognition information. The processor 13 can predict the motion path of a moving object based on its current speed, direction, and position.

[0097] Processor 13 can estimate how a static object can maintain its current position.

[0098] The processor 13 can determine the expected path of an external object based on the results of modeling the type and behavior of the external object.

[0099] Processor 13 can estimate the movement of a moving object classified as a vehicle (e.g., a car, motorcycle, etc.) along a road lane. In the case of moving objects such as vehicles, motorcycles, etc., processor 13 can determine the predicted path by taking into account the current speed and direction (heading angle) of the moving object as well as the curvature and directionality of the lane.

[0100] For example, when a vehicle, as a moving object, is moving in a straight line, the processor 13 can predict that the moving object will continue to move in a straight line while maintaining its current speed and steering angle, and then travel along a lane that it gradually enters according to the curvature of the road.

[0101] The processor 13 can estimate that a moving object classified as a pedestrian will not move along the lane, but will move while complying with road rules (e.g., using a crosswalk or walking along a sidewalk), and determine the expected path of the external object accordingly.

[0102] For example, processor 13 can estimate that a pedestrian will move along the sidewalk while maintaining the current speed, estimate that the pedestrian will change direction and cross the road as they approach the crosswalk, and determine the expected path of external objects based on the estimation.

[0103] Predictions can be made based on the motion patterns of external objects and road structures (such as the location of pedestrian crossings and sidewalks).

[0104] Processor 13 can estimate that external objects classified as animals will move regardless of road rules and determine the expected path of the external objects.

[0105] For example, processor 13 can predict the path that the animal is most likely to move randomly in the direction of its current movement, based on the results of behavioral modeling (which is determined based on the position information of external objects detected in a specific time period in the past), and introduce greater uncertainty into the predicted path.

[0106] The processor 13 can update the predicted path of external objects in real time based on the external object recognition information collected in real time.

[0107] The processor 13 can determine the control strategy (also known as the vehicle control strategy) of the master vehicle for external objects that are expected to interfere.

[0108] The predicted path of an external object described in this article can refer to the process of predicting the future position based on the current position, velocity, and orientation data of the external object. Processor 13 can analyze the probability that the predicted path of the external object will enter the vehicle's driving path (including the driving lane and safety zone).

[0109] The processor 13 can compare the projected path of an external object with the driving path of the main vehicle to assess the likelihood that the external object will affect the vehicle's speed, direction, or control (acceleration, deceleration, and steering).

[0110] The processor 13 can determine the relative position and timing of the vehicle and external objects based on the vehicle's acceleration / deceleration, steering response, speed changes, etc., and can establish a control strategy based on calculations, so that the vehicle can effectively select a driving path while avoiding interference from external objects.

[0111] A control strategy can be defined as a combination of longitudinal control (acceleration / deceleration) and lateral control (steering). Furthermore, control strategies can be expressed in the form of following, overtaking, lateral movement, lane changing, etc., on the road.

[0112] The processor 13 selects the most appropriate control strategy (overtaking or following) under specific conditions to avoid interference and achieve the driving purpose (also known as the trip type). For example, the driving purpose can be one of the following: personal travel, business travel, emergency travel (e.g., travel to the emergency room, first responder to an emergency scene, etc.), delivery travel (e.g., trucking goods), etc. The control strategy can be determined based on the trip type pre-determined or set by the user (e.g., the driver). For example, if the user sets the driving purpose to emergency travel, a more aggressive vehicle control strategy is adopted (e.g., prioritizing shorter routes over more energy-efficient routes).

[0113] The processor 13 can determine a control strategy based on the predicted path of external objects and vehicle driving information about the vehicle.

[0114] For example, processor 13 can determine a control strategy by combining whether to perform lateral control on an external object that is expected to interfere, whether to perform overtaking, and whether to perform following.

[0115] The driving path of the main vehicle can be defined as an additional safety zone that takes into account the current lane, the width of the vehicle, etc.

[0116] The processor 13 can identify situations where the expected path of an external object may enter the vehicle's driving path as interference. In interference situations, the processor 13 can establish a control strategy by analyzing the relative position and speed of the external object and the vehicle, as well as the expected entry time.

[0117] For example, depending on the interference from external objects, processor 13 can determine a first strategy of following without lateral control, a second strategy of overtaking without lateral control, a third strategy of following by performing lateral control, or a fourth strategy of overtaking by performing lateral control for external objects expected to interfere.

[0118] When an external object enters the interference zone earlier than the vehicle and the vehicle is behind the object and needs to follow it, the processor 13 can determine a first strategy for following without lateral control. For example, the processor 13 can analyze the relative position and speed of the external object and the vehicle to determine the expected interference zone and the expected time of interference. When the processor 13 determines that the vehicle may not be able to cross the interference zone before the expected time of interference through lateral control and acceleration, the processor 13 can determine the first strategy as the vehicle's control strategy. That is, when the time the external object enters the interference zone is earlier than the time the vehicle enters the interference zone by a preset first threshold or greater, the processor 13 can determine the first strategy of following without lateral control as the vehicle's control strategy. The processor 13 can set the first threshold based on the relative position and speed of the external object and the vehicle.

[0119] When the vehicle can pass through a corresponding area before an external object enters its driving path, the processor 13 can determine a second strategy for overtaking without lateral control. For example, the processor 13 can analyze the relative position and speed of the external object and the vehicle to determine the expected interference area and the expected time of interference. When the processor 13 determines that the vehicle can pass through the interference area before the expected time of interference simply by accelerating without lateral control, the processor 13 can determine the second strategy of overtaking without lateral control as the vehicle's control strategy. That is, when the time the vehicle enters the interference area is earlier than a preset second threshold or greater than the time the external object enters the interference area, the processor 13 can determine the second strategy of overtaking without lateral control as the vehicle's control strategy. The processor 13 can set the second threshold based on the relative position and speed of the external object and the vehicle.

[0120] When an external object and the vehicle enter the interference zone almost simultaneously, but the vehicle is behind the object and needs to follow it, the processor 13 can determine a third strategy for following by performing lateral control. For example, the processor 13 can analyze the relative position and speed of the external object and the vehicle to determine the expected interference zone and the expected time of interference. When the processor 13 determines that the vehicle may not be able to cross the interference zone before the expected time of interference through lateral control and acceleration, the processor 13 can determine the third strategy of following by performing lateral control as the vehicle's control strategy. For example, the processor 13 can perform a certain degree of lateral control to avoid a collision with the external object because the external object and the vehicle enter the interference zone at almost the same time. That is, when the time of entry of the external object into the interference zone is earlier than the time of entry of the vehicle into the interference zone and less than or equal to a preset third threshold, the processor 13 can determine the third strategy of following by performing lateral control as the vehicle's control strategy. The processor 13 can set the third threshold based on the relative position and speed of the external object and the vehicle.

[0121] When an external object and the vehicle enter the vehicle's path almost simultaneously, but the vehicle can use parallel lateral control and acceleration to pass through the interference zone first ahead of the external object, the processor 13 can determine a fourth strategy for overtaking by performing lateral control. For example, the processor 13 can analyze the relative position and speed of the external object and the vehicle to determine the interference zone where interference with the external object is expected and the expected time of interference. When the processor 13 determines that the vehicle can pass through the interference zone before the interference time point by performing lateral control and acceleration, the processor 13 can determine the fourth strategy for overtaking by performing lateral control as the vehicle's control strategy. That is, when the time point when the vehicle enters the interference zone is earlier than the time point when the external object enters the interference zone and is less than or equal to a preset fourth threshold, the processor 13 can determine the fourth strategy for overtaking by performing lateral control as the main vehicle's control strategy. The processor 13 can set the fourth threshold based on the relative position and speed of the external object and the main vehicle.

[0122] In addition, the processor 13 can adjust the control strategy by taking into account road speed limits (e.g., marked speed limits) and regulatory information (e.g., local traffic regulations).

[0123] In addition, the processor 13 can adjust the control strategy by taking into account the preset driving purpose.

[0124] Control strategies can be determined based on driving information about the vehicle and the expected paths of external objects, but they can also be determined by taking into account other conditions and driving objectives.

[0125] For example, processor 13 can establish a control strategy based on road curvature while adhering to speed limits, traffic regulations, safe distances, etc.

[0126] In addition, the processor 13 can establish a control strategy by applying additional conditions such as minimum bump curves to maintain ride comfort, acceleration / deceleration curves that take energy efficiency into account, and the shortest travel time to the target point.

[0127] For example, when determining a second strategy for overtaking without lateral control or a fourth strategy for overtaking by performing lateral control, if the speed required for overtaking exceeds the speed limit of the road, the processor 13 can choose to follow the strategy instead of the second or fourth strategy.

[0128] For example, processor 13 can select a control strategy in the direction that minimizes bumps on roads with low speed limits, and can select a control strategy in the direction that reaches the target point in the shortest time on highways.

[0129] The processor 13 can select the most suitable control strategy and execute the acceleration / deceleration and steering control of the main vehicle according to the selected control strategy.

[0130] Processor 13 can modify the control strategy by adjusting the predicted path of the external object based on motion information about the external object. For example, processor 13 can adjust the predicted path (e.g., predicted motion path) of the external object based on updated external object information. Processor 13 can modify the control strategy (e.g., vehicle control strategy) based on the updated predicted motion path of the external object. That is, processor 13 can determine motion information about the external object based on real-time detected external object identification information, and maintain or change the current control strategy based on the determined motion information about the external object.

[0131] Figure 4 This is a diagram illustrating an example operation of processor 13. (Reference) Figure 4 The processor 13 can select a strategy for following a first external object that is expected to interfere without lateral control, and determine a strategy for overtaking a second external object using lateral control. The processor 13 can utilize driving information about the vehicle and select a candidate position group for the vehicle by considering position conditions L1 for each time period of following the first external object without lateral control and position conditions L2 for each time period of overtaking the second external object using lateral control. The processor 13 can determine the longitudinal and lateral control quantities of the vehicle that satisfy the candidate position group, and determine multiple position curves C_group that match the vehicle's current driving path when the vehicle travels according to the determined control quantities. For example, the processor 13 can select the position curve C_select with the least bump among the multiple position curves and determine the vehicle's control quantity based on the selected position curve.

[0132] The processor 13 can visually display, via the display device 11, the expected driving area (also known as the planned driving path) that the vehicle is to travel according to the control strategy.

[0133] For example, the processor 13 can adjust at least one of the area, color, contrast, brightness, shape, and chromaticity of the expected driving area based on the expected path of the external object and the control strategy.

[0134] Furthermore, if the control strategy is modified, the processor 13 can adjust at least one of the area, color, contrast, brightness, shape, and chromaticity of the expected driving area according to the modified control strategy.

[0135] The processor 13 can define the space that visually represents the path that the vehicle can travel as the vehicle's expected driving area. The processor 13 can dynamically generate the expected driving path based on the vehicle's kinematic characteristics (speed, acceleration, steering angle, etc.), road conditions (lanes, curvature, signals, etc.) and the expected paths of external objects.

[0136] When there is a possibility that an external object may interfere with the vehicle's driving path, the processor 13 can exclude the expected path of the external object from the vehicle's driving area. For example, when the vehicle uses lateral control to overtake or follow an external object, the processor 13 can adjust the size and position of the expected driving area to avoid the expected path of the object. The processor 13 can display the expected driving area in the vehicle's current driving area, excluding the interference area expected to interfere with the external object. In other words, the processor 13 can adjust the expected driving area so that the vehicle's driving path and the expected path of the external object do not overlap.

[0137] In addition, the processor 13 can display the expected driving area in different colors according to the vehicle's acceleration and deceleration.

[0138] When an external object may overlap with the vehicle's path, but the vehicle is overtaking or following the external object without lateral control, the processor 13 can adjust the color. For example, when it is determined that the vehicle will pass through the interference area without lateral control before the point of interference with the external object, the processor 13 can display different colors of the expected driving area based on the vehicle's acceleration and deceleration, without adjusting the size or position of the current driving area.

[0139] For example, when the vehicle needs to accelerate, the processor 13 can make the color of the driving area brighter or highlight the boundary lines to indicate that the vehicle plans to accelerate through the driving path.

[0140] For example, when deceleration or lateral control is required, processor 13 can adjust the boundaries of the driving area to visually highlight the new path.

[0141] In addition, when vehicle acceleration is required, the processor 13 can brightly display the color of the expected driving area, or adjust the brightness and shape to emphasize specific situations.

[0142] The processor 13 can update the predicted driving area in real time based on the movement of the vehicle and nearby objects. For example, if the vehicle in front suddenly decelerates or a new obstacle is detected, the processor 13 can take this into account and immediately reset the predicted driving area.

[0143] Figure 5 , Figure 6 , Figure 7This is a diagram illustrating an example control operation of processor 13.

[0144] refer to Figure 5 In (a), vehicle Ve is traveling using an autonomous driving system, and an external object (hereinafter, "entering vehicle O1") is expected to enter the driving path area of ​​vehicle Ve. Processor 13 sets the initial control strategy of vehicle Ve to overtake without lateral control, which determines that vehicle Ve will accelerate and cross the driving path before entering vehicle O1. Processor 13 displays on display device 11 the expected driving area area_1, which ensures a safe distance from the rubber cone O2, which is a stationary object ahead.

[0145] refer to Figure 5 In (b), the speed of the entering vehicle O1 begins to increase more than the speed of the current vehicle Ve, and the processor 13 determines that the current vehicle Ve has difficulty crossing the path before the entering vehicle O1 without lateral control. The processor 13 changes the control strategy to one that uses the biased driving of the current vehicle Ve to overtake the entering vehicle O1. Based on the changed control strategy, the processor 13 removes the interference area expected to interfere with the entering vehicle O1 from the expected driving area area_1 and displays a new expected driving area area_1. For example, the processor 13 can adjust at least one of the brightness, color, and chromaticity of the expected driving area area_1 based on changes in the acceleration / deceleration control amount of the current vehicle Ve.

[0146] refer to Figure 5 In (c), the speed of the entering vehicle O1 increases more than before, therefore processor 13 determines that it is difficult for the current vehicle Ve to overtake the entering vehicle O1. Processor 13 abandons the strategy of overtaking the entering vehicle O1 and changes the control strategy to follow the entering vehicle O1 using lateral control. Based on the changed control strategy, processor 13 further removes the interference area expected to interfere with the entering vehicle O1 from the expected driving area and displays a new expected driving area area_1. For example, processor 13 can adjust at least one of the brightness, color, and chromaticity of the expected driving area area_1 based on changes in the acceleration / deceleration control amount of the current vehicle Ve.

[0147] refer to Figure 5In case (d), the speed of the entering vehicle O1 increases significantly compared to before, so the processor 13 will change its control strategy to follow the entering vehicle O1 even without performing lateral control. For example, the entering vehicle O1 enters the driving path of the vehicle Ve without affecting the driving of the vehicle Ve. Therefore, the processor 13 will not adjust the size or shape of the expected driving path of the vehicle Ve based on the expected path of the entering vehicle O1. However, the processor 13 can adjust at least one of the brightness, color, and chromaticity of the expected driving area area_1 based on the changes in the acceleration / deceleration control amount of the vehicle Ve.

[0148] refer to Figure 6 The processor 13 plans to change the lane of vehicle Ve to avoid the rubber cone O4 located in the driving path of vehicle Ve, and predicts that the entering vehicle O3 will enter the lane that vehicle Ve plans to change to based on external object recognition information.

[0149] exist Figure 6 In (a), processor 13 sets the control strategy to overtake the entering vehicle O3 without lateral control and determines that vehicle Ve is in a position to accelerate and change lanes before entering vehicle O3. Here, the strategy of overtaking without lateral control refers to the strategy for entering vehicle O3, and lateral control needs to be performed during the lane change. Processor 13 can display the expected travel path of vehicle Ve through the lane change on display device 11.

[0150] exist Figure 6 In (b), as the speed of the entering vehicle O3 gradually increases, the processor 13 determines that there is a high probability of interference with the expected travel path of the vehicle Ve. The processor 13 can detect this and change the control strategy to overtake using lateral control. In this way, the processor 13 can attempt to complete the lane change before entering vehicle O3. The processor 13 removes the interference area expected to interfere with the entering vehicle O3 from the expected travel area area_2 according to the changed control strategy and displays a new expected travel area area_2. For example, the processor 13 can adjust at least one of the brightness, color, and chromaticity of the expected travel area area_2 according to changes in the acceleration / deceleration control amount of the vehicle Ve.

[0151] exist Figure 6In step (c), the speed of the entering vehicle O3 further increases, so the processor 13 can cancel the lane change and change the control strategy to follow without lateral control. The processor 13 modifies the expected travel path (e.g., the planned travel path of the vehicle) to the existing lane (e.g., the vehicle's current travel lane), and then controls the vehicle Ve until the entering vehicle O3 overtakes the vehicle Ve in the lane to be changed and moves away from the vehicle at a safe distance or further. The processor 13 can then determine the expected travel path to perform the lane change to follow behind the entering vehicle O3. The processor 13 can reset the expected travel area area_2 to follow behind the entering vehicle O3 according to the changed control strategy, and display the new expected travel area on the display device 11. For example, the processor 13 can adjust at least one of the brightness, color, and chromaticity of the expected travel area area_2 according to changes in the acceleration / deceleration control amount of the vehicle Ve.

[0152] refer to Figure 7 Vehicle Ve is crossing an intersection and is expected to enter vehicle O5's path.

[0153] exist Figure 7 In (a), processor 13 determines that vehicle Ve will cross the intersection before entering vehicle O5 according to a strategy of overtaking without lateral control. Processor 13 can display the expected driving path of vehicle Ve to cross the intersection on display device 11.

[0154] exist Figure 7 In (b), the entering vehicle O5 approaches the intersection faster than the vehicle Ve. The processor 13 determines that the vehicle Ve is unlikely to cross the intersection as originally planned and changes the control strategy to follow using lateral control. Based on the changed control strategy, the processor 13 removes the interference area expected to interfere with the entering vehicle O5 from the expected driving area area_3 and displays a new expected driving area area_3. For example, the processor 13 can adjust at least one of the brightness, color, and chromaticity of the expected driving area area_3 based on changes in the acceleration / deceleration control amount of the vehicle Ve.

[0155] exist Figure 7In (c), the speed of the entering vehicle O5 gradually increases, thereby increasing the likelihood of it overtaking the vehicle Ve. The processor 13 can then change its control strategy to follow without lateral control to prevent a collision with the entering vehicle O5. In this way, the processor 13 can modify the expected travel path of the vehicle Ve within the intersection, following the direction of the entering vehicle O5. The processor 13 can reset the expected travel area area_3 to follow behind the entering vehicle O5 based on the changed control strategy, and display the new expected travel area on the display device 11. For example, the processor 13 can adjust at least one of the brightness, color, and chromaticity of the expected travel area area_3 based on changes in the acceleration / deceleration control amount of the vehicle Ve.

[0156] Figure 8 This is a flowchart of an example method for controlling a vehicle. (Reference) Figure 8 The processor can determine the expected path of the external object based on the type of the external object and the motion information about the external object included in the external object identification information (step S801).

[0157] The processor can determine the external object that is expected to interfere based on the expected path of the external object and the vehicle driving information about the vehicle (step S802).

[0158] The processor can determine a control strategy based on the expected path of an external object and vehicle driving information about the vehicle. For example, for an external object that is expected to interfere, the processor can determine a first strategy of following without lateral control, a second strategy of overtaking without lateral control, a third strategy of following by performing lateral control, or a fourth strategy of overtaking by performing lateral control (step S803).

[0159] The processor can visually display the expected driving area that the vehicle is expected to travel according to the control strategy via a display device. In this way, the processor can adjust at least one of the area, color, contrast, brightness, shape, and chromaticity of the expected driving area according to the expected path of external objects and the control strategy (step S804).

[0160] The processor can modify the control strategy by taking into account the external object recognition information collected in real time and the driving information about the vehicle, and update and display the expected driving area according to the modified control strategy (step S805).

[0161] The term "unit" used in the example implementation refers to a software component or a hardware component such as a field-programmable gate array (FPGA) or application-specific integrated circuit (ASIC), and a "unit" performs a specific function. However, a "unit" is not limited to software or hardware. A "unit" may be configured to reside in an addressable storage medium or may be configured to reproduce one or more processors. Thus, for example, a "unit" includes components such as software components, object-oriented software components, class components, and task components, and includes processes, functions, attributes, procedures, subroutines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided in components and "units" may be combined into a smaller number of components and "units," or may be further divided into additional components and "units." Furthermore, components and "units" may be implemented as one or more CPUs in a playback device or a secure multimedia card.

[0162] According to the present invention, a vehicle control device is provided, comprising a display device, a sensor unit, one or more processors, and a memory storing one or more programs executed by the one or more processors, wherein the sensor unit collects vehicle driving information and external object identification information about the vehicle, and the processors are configured to determine, based on the vehicle driving information and external object identification information, external objects expected to interfere with the vehicle's driving path, determine a control strategy for the vehicle for the expected interfering external objects, and visually display, through the display device, the expected driving area to be driven by the vehicle determined according to the control strategy.

[0163] The processor can determine the expected path of an external object based on the type of the external object and motion information about the external object included in the external object identification information.

[0164] The processor can determine the external objects that are expected to interfere based on the predicted path of the external objects and vehicle driving information about the vehicle.

[0165] The processor can adjust at least one of the area, color, contrast, brightness, shape, and chromaticity of the expected driving area based on the expected path of the external object and the control strategy.

[0166] The processor can be configured to modify the control strategy by adjusting the expected path of the external object based on motion information about the external object, and to adjust at least one of the area, color, contrast, brightness, shape, and chromaticity of the expected driving area according to the modified control strategy.

[0167] The processor can determine the control strategy based on the predicted path of external objects and vehicle driving information about the vehicle.

[0168] The processor can determine the control strategy by combining whether to perform lateral control, whether to perform overtaking, and whether to perform following in response to expected interference from external objects.

[0169] For an external object that is expected to interfere, the processor can determine a first strategy of following without lateral control, a second strategy of overtaking without lateral control, a third strategy of following by performing lateral control, or a fourth strategy of overtaking by performing lateral control.

[0170] The processor can adjust the control strategy by taking into account road speed limits and regulatory information.

[0171] The processor can adjust the control strategy by taking into account the preset driving purpose.

[0172] According to the present invention, a vehicle control method executed by a computing device is provided, the computing device having one or more processors and a memory storing one or more programs executed by the one or more processors, the method comprising: the processor determining, based on vehicle driving information and external object identification information collected by a sensor unit, external objects expected to interfere with the driving path of the vehicle; the processor determining a control strategy for the vehicle in response to the expected external objects; and the processor visually displaying, via a display device, the expected driving area to be driven by the vehicle determined according to the control strategy.

[0173] The processor can calculate the predicted path of an external object based on the type of the external object and motion information about the external object included in the external object identification information.

[0174] The processor can determine the external objects that are expected to interfere based on the predicted path of the external objects and vehicle driving information about the vehicle.

[0175] The processor can adjust at least one of the area, color, contrast, brightness, shape, and chromaticity of the expected driving area based on the expected path of the external object and the control strategy.

[0176] The processor can modify the control strategy by adjusting the expected path of the external object based on motion information about the external object, and adjust at least one of the area, color, contrast, brightness, shape, and chromaticity of the expected driving area according to the modified control strategy.

[0177] The processor can determine the control strategy based on the predicted path of external objects and vehicle driving information about the vehicle.

[0178] The processor can determine the control strategy by combining whether to perform lateral control, whether to perform overtaking, and whether to perform following in response to expected interference from external objects.

[0179] The processor can determine a first strategy of following an external object that is expected to interfere with the vehicle without lateral control, a second strategy of overtaking without lateral control, a third strategy of following by performing lateral control, or a fourth strategy of overtaking by performing lateral control.

[0180] The processor can adjust the control strategy by taking into account road speed limits and regulatory information.

[0181] The processor can adjust the control strategy by taking into account the preset driving purpose.

[0182] The vehicle control device and method according to the present invention can intuitively display the control strategy of an autonomous vehicle.

[0183] Furthermore, the vehicle's driving strategy can be determined by taking into account interference from external objects, and the determined driving strategy can be visualized.

[0184] In addition, the vehicle's driving strategy can be updated and displayed in real time when the driving strategy changes.

[0185] Although one or more exemplary embodiments of the invention have been described herein, it should be understood that various changes and modifications can be made to the invention by those skilled in the art without departing from the spirit and scope of the invention as set forth in the appended claims.

Claims

1. A vehicle control device for a vehicle, the vehicle control device comprising: Display device; One or more sensors, the one or more sensors being configured to acquire vehicle driving information about the vehicle and external object information about external objects; One or more processors; as well as A memory storing at least one instruction configured to, when executed by one or more processors communicating with the memory, cause the vehicle control device to: Based on vehicle driving information and external object information, identify external objects whose probability scores are higher than a threshold, indicating the possibility of interference with the vehicle's driving path. The vehicle control strategy is determined based on external objects whose probability scores are higher than a threshold. The vehicle's planned autonomous driving path is displayed via a display device and based on the vehicle control strategy.

2. The vehicle control device for a vehicle according to claim 1, wherein The external object information includes motion information about the external object, and wherein the at least one instruction is configured to, when executed by one or more processors communicating with the memory, further cause the vehicle control device to: Based on the object type of the external object and the motion information about the external object, the expected motion path of the external object is determined.

3. The vehicle control device for a vehicle according to claim 2, wherein The at least one instruction is configured to, when executed by one or more processors communicating with the memory, further cause the vehicle control device to: A probability score is determined based on the predicted motion path of external objects and vehicle driving information.

4. The vehicle control device for a vehicle according to claim 2, wherein The at least one instruction is configured to, when executed by one or more processors communicating with the memory, cause the vehicle control device to display the vehicle's planned autonomous driving path in such a way as follows: Based on the predicted motion path of external objects and the vehicle control strategy, determine at least one of the area, color, contrast, brightness, shape, or chromaticity of the planned autonomous driving path displayed on the display device.

5. The vehicle control device for a vehicle according to claim 2, wherein The at least one instruction is configured to, when executed by one or more processors communicating with the memory, cause the vehicle to display its planned autonomous driving path in such a way as follows: Based on the updated information about the external object, adjust the predicted motion path of the external object. Based on the adjusted predicted motion path of the external object, modify the vehicle control strategy; Based on the modified vehicle control strategy, at least one of the area, color, contrast, brightness, shape, or chromaticity of the planned autonomous driving path displayed on the display device is adjusted.

6. The vehicle control device for a vehicle according to claim 2, wherein The at least one instruction is configured to, when executed by one or more processors communicating with the memory, cause the vehicle to determine its vehicle control strategy in such a way as follows: The vehicle control strategy is further determined based on the predicted motion path of external objects and vehicle driving information.

7. The vehicle control device for a vehicle according to claim 1, wherein The at least one instruction is configured to, when executed by one or more processors communicating with the memory, cause the vehicle to determine a vehicle control strategy in the following manner: Determine at least one of the following: Whether to implement lateral control of the vehicle. Does it exceed the external object, or Whether to follow external objects.

8. The vehicle control device for a vehicle according to claim 1, wherein The at least one instruction is configured to, when executed by one or more processors communicating with the memory, cause the vehicle to determine a vehicle control strategy in the following manner: Determine at least one of the following: This includes a primary vehicle control strategy that follows external objects without exercising lateral control of the vehicle. This includes a second vehicle control strategy that allows the vehicle to overtake external objects without performing lateral control. This includes third vehicle control strategies that follow external objects and perform lateral control of the vehicle, or This includes a fourth vehicle control strategy that allows the vehicle to move beyond external objects and perform lateral control of the vehicle.

9. The vehicle control device for a vehicle according to claim 1, wherein The at least one instruction is configured to, when executed by one or more processors communicating with the memory, cause the vehicle to determine a vehicle control strategy in the following manner: Adjust vehicle control strategies based on at least one of road speed limits or traffic regulations.

10. The vehicle control device for a vehicle according to claim 1, wherein The at least one instruction is configured to, when executed by one or more processors communicating with the memory, cause the vehicle to determine a vehicle control strategy in the following manner: Adjust vehicle control strategies based on the driving purpose associated with the vehicle.

11. A method performed by a device of a vehicle, the method comprising: Vehicle driving information and external object information are obtained through one or more sensors of the vehicle. Based on vehicle driving information and external object information, identify external objects whose probability scores are higher than a threshold, indicating the possibility of interference with the vehicle's driving path. The vehicle control strategy is determined based on external objects whose probability scores are higher than a threshold. The vehicle's planned autonomous driving path is displayed via a display device and based on the vehicle control strategy.

12. The method of claim 11, wherein, The external object information includes motion information about the external object, and the method further includes: Based on the object type of the external object and the motion information about the external object, the expected motion path of the external object is determined.

13. The method of claim 12, further comprising: A probability score is determined based on the predicted motion path of external objects and vehicle driving information.

14. The method of claim 12, wherein, The planned autonomous driving path displayed for the vehicle includes: Based on the predicted motion path of external objects and the vehicle control strategy, determine at least one of the area, color, contrast, brightness, shape, or chromaticity of the planned autonomous driving path displayed on the display device.

15. The method of claim 12, wherein, The planned autonomous driving path displayed for the vehicle includes: Based on the updated information about the external object, adjust the predicted motion path of the external object. Based on the adjusted predicted motion path of the external object, modify the vehicle control strategy; Based on the modified vehicle control strategy, at least one of the area, color, contrast, brightness, shape, or chromaticity of the planned autonomous driving path displayed on the display device is adjusted.

16. The method of claim 12, wherein, Determining the vehicle control strategy includes: The vehicle control strategy is further determined based on the predicted motion path of external objects and vehicle driving information.

17. The method of claim 11, wherein, Determining the vehicle control strategy includes: Determine at least one of the following: Whether to implement lateral control of the vehicle. Does it exceed the external object, or Whether to follow external objects.

18. The method of claim 11, wherein, Determining the vehicle control strategy includes: Determine at least one of the following: This includes a primary vehicle control strategy that follows external objects without exercising lateral control of the vehicle. This includes a second vehicle control strategy that allows the vehicle to overtake external objects without performing lateral control. This includes third vehicle control strategies that follow external objects and perform lateral control of the vehicle, or This includes a fourth vehicle control strategy that allows the vehicle to move beyond external objects and perform lateral control of the vehicle.

19. The method of claim 17, wherein, Determining the vehicle control strategy includes: Adjust vehicle control strategies based on at least one of road speed limits or traffic regulations.

20. The method of claim 17, wherein, Determining the vehicle control strategy includes: Adjust vehicle control strategies based on the driving purpose associated with the vehicle.

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