Autonomous driving vehicle and control method thereof

Abstract

An autonomous driving vehicle may include one or more sensors configured to detect at least one vehicle, a memory, and a processor. The processor may be configured to set a leader vehicle of the at least one vehicle as a target vehicle. The leader vehicle may travel in front of the vehicle. The processor may be further configured to: receive driving information related to a target vehicle via one or more sensors; determining the possibility that the target vehicle is cut out of the lane based on the driving information and a predetermined cut-out condition; and controlling the vehicle to operate in one of a plurality of safety control modes based on the determined likelihood.

Description

Technical Field

[0001] The present invention relates to an automatic driving vehicle and a control method thereof. Background Art

[0002] A vehicle is a device that can transport people or goods to a destination while traveling on roads or tracks. A vehicle can be moved from one location to another using one or more wheels mounted on its body. Vehicles can include three- or four-wheeled vehicles, two-wheeled vehicles (such as motorcycles), as well as construction machinery, bicycles, and trains that travel on rails arranged on tracks.

[0003] In modern society, vehicles (such as ground vehicles) are one of the most common means of transportation, and the number of people using them is increasing. While advances in vehicle technology have made long-distance travel easier and improved people's quality of life, they have also led to the deterioration of road traffic conditions in densely populated areas such as South Korea, often resulting in severe traffic congestion. Summary of the Invention

[0004] The object of the present invention is to provide an autonomous driving vehicle and a control method thereof, which can use sensors such as a front camera, a front radar and a front side lidar to determine the behavior of a preceding vehicle traveling in front of the autonomous driving vehicle, and change the brake control time based on the determination result.

[0005] The technical objectives to be achieved by the present invention are not limited to the above technical objectives, and those skilled in the art can also clearly understand other technical objectives not described above through the following description.

[0006] A vehicle according to one or more exemplary embodiments of the present invention may include: one or more sensors configured to detect at least one vehicle; a memory configured to store computer-readable instructions; and a processor configured to execute the computer-readable instructions. By executing the computer-readable instructions, the processor may be configured to: set a leading vehicle among the at least one vehicle as a target vehicle; receive driving information associated with the target vehicle via the one or more sensors; determine a likelihood of the target vehicle cutting out of a lane based on the driving information and a predetermined cut-out condition; and, based on the determined likelihood, control the vehicle of the present invention to operate in one of a plurality of safety control modes. The leading vehicle may travel in front of the vehicle of the present invention.

[0007] The driving information may indicate at least one of: a lateral position of the target vehicle, a lateral speed of the target vehicle, a lateral direction of the target vehicle, a path of the target vehicle, or a path of the present invention vehicle.

[0008] The processor can be further configured to: determine a first movement index based on the lateral position of the target vehicle; determine a second movement index based on the lateral speed of the target vehicle; determine a motion index based on the lateral direction of the target vehicle; and determine a collision index based on the path of the target vehicle and the path of the vehicle of the present invention.

[0009] The plurality of safety control modes may include a first safety control mode and a second safety control mode. The second safety control mode may require a faster response from the vehicle of the present invention than from the first safety control mode. The processor may be configured to control the vehicle of the present invention to operate in the safety control mode by controlling the vehicle of the present invention in the second safety control mode based on the first movement index, the second movement index, the motion index, and the collision index satisfying a predetermined cut-out condition.

[0010] The plurality of safety control modes may include a first safety control mode and a second safety control mode. The second safety control mode may require a faster response from the vehicle than from the first safety control mode. The processor may be configured to control the vehicle to operate in the safety control mode by controlling the vehicle in the first safety control mode based on at least one of the first movement index, the second movement index, the motion index, or the collision index failing to satisfy a predetermined cut-out condition.

[0011] The brake control time associated with the second safety control mode may be different from the brake control time associated with the first safety control mode.

[0012] The brake control time associated with the second safety control mode may be shorter than the brake control time associated with the first safety control mode.

[0013] The brake control times associated with the second safety control mode may include a collision warning time, a first emergency braking time, and a second emergency braking time. The processor may be further configured to change the collision warning time, the first emergency braking time, and the second emergency braking time based on the distance between the target vehicle and the control target sensed after the target vehicle exits the lane.

[0014] The processor may be further configured to set the lead vehicle as the target vehicle based on the lead vehicle and the vehicle of the present invention traveling in the same lane.

[0015] According to one or more exemplary embodiments of the present invention, a method performed by an apparatus of a vehicle of the present invention may include: detecting at least one vehicle via one or more sensors of the vehicle of the present invention; setting a leading vehicle among the at least one vehicle as a target vehicle; receiving driving information associated with the target vehicle via the one or more sensors; determining a likelihood of the target vehicle cutting out of a lane based on the driving information and a predetermined cut-out condition; and, based on the determined likelihood, controlling the vehicle of the present invention to operate in one of a plurality of safety control modes. The leading vehicle may be traveling ahead of the vehicle of the present invention.

[0016] The driving information may indicate at least one of: a lateral position of the target vehicle, a lateral speed of the target vehicle, a lateral direction of the target vehicle, a path of the target vehicle, or a path of the present invention vehicle.

[0017] The method of the present invention may further include: determining a first movement index based on the lateral position of the target vehicle; determining a second movement index based on the lateral speed of the target vehicle; determining a motion index based on the lateral direction of the target vehicle; and determining a collision index based on the path of the target vehicle and the path of the vehicle of the present invention.

[0018] The plurality of safety control modes may include a first safety control mode and a second safety control mode. The second safety control mode may require a faster response from the vehicle of the present invention than from the first safety control mode. Controlling the vehicle of the present invention to operate in the safety control mode may include controlling the vehicle of the present invention in the second safety control mode based on the first movement index, the second movement index, the motion index, and the collision index satisfying a predetermined cut-out condition.

[0019] The plurality of safety control modes may include a first safety control mode and a second safety control mode. The second safety control mode may require a faster response from the vehicle than in the first safety control mode. Controlling the vehicle to operate in the safety control mode may include controlling the vehicle in the first safety control mode based on at least one of the first movement index, the second movement index, the motion index, or the collision index failing to satisfy a predetermined cut-out condition.

[0020] The brake control time associated with the second safety control mode may be different from the brake control time associated with the first safety control mode.

[0021] The brake control time associated with the second safety control mode may be shorter than the brake control time associated with the first safety control mode.

[0022] The brake control time associated with the second safety control mode may include a collision warning time, a first emergency braking time, and a second emergency braking time. The method of the present invention may further include: changing the collision warning time, the first emergency braking time, and the second emergency braking time based on a distance between the target vehicle and the control target sensed after the target vehicle exits the lane.

[0023] The method of the present invention may further include: setting the leading vehicle as the target vehicle based on the leading vehicle and the vehicle of the present invention traveling in the same lane.

[0024] The effects that can be achieved by the present invention are not limited to the above effects, and those skilled in the art can also clearly understand other effects not described above through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a block diagram illustrating an autonomous vehicle.

[0026] Figure 2 is a flow chart illustrating a method of controlling an autonomous vehicle.

[0027] Figure 3 is a flowchart illustrating a method of calculating a first movement index (Movement Index 1) based on lateral position information of a target vehicle.

[0028] Figure 4 is a flowchart illustrating a method of calculating a second movement index (Movement Index 2) based on lateral speed information of a target vehicle.

[0029] Figure 5 and Figure 6 is a schematic diagram illustrating a method of calculating a motion index based on lateral direction information of a target vehicle.

[0030] Figure 7 is a schematic diagram illustrating a method of calculating a collision index based on a target vehicle's path and an (ego) vehicle's path.

[0031] Figure 8A and Figure 8B is a schematic diagram showing an example of operation in the first safety control mode.

[0032] Figure 9A and Figure 9B is a schematic diagram showing an example of operation in the second safety control mode.

[0033] Figure 10A and Figure 10B is a schematic diagram showing an autonomous driving vehicle operating in a second safety control mode. DETAILED DESCRIPTION

[0034] Hereinafter, one or more exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. Identical or similar elements will be given the same reference numerals regardless of the reference numerals, and their repeated description will be omitted. Furthermore, in the following description of the exemplary embodiments, if it is determined that a detailed description of a related known art makes the key points of the exemplary embodiments described herein unclear, the detailed description will be omitted.

[0035] As used herein, the terms "include," "comprising," and "having" specify the presence of the stated features, numbers, operations, elements, components, and / or combinations thereof, but do not preclude the presence or addition of one or more other features, numbers, operations, elements, components, and / or combinations thereof. Furthermore, when describing one or more exemplary embodiments with reference to the accompanying drawings, the same reference numerals denote the same components, and repeated description thereof will be omitted.

[0036] For purposes of this application and the claims, the exemplary phrases "at least one of A; B; or C" or "at least one of A, B, or C" are used, which phrases mean "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." In addition, as used herein, exemplary phrases such as "A, B, and C," "A, B, or C," "at least one of A, B, and C," "at least one of A, B, or C," etc. may refer to each listed item or all possible combinations of the listed items. For example, "at least one of A or B" may mean (1) at least one A; (2) at least one B; or (3) at least one A and at least one B.

[0037] In recent years, there has been active research on vehicles equipped with advanced driver assistance systems (ADAS), which proactively provide information about the vehicle status, driver status, and surrounding environment to reduce the driver's burden and improve convenience.

[0038] ADAS provided in vehicles may include, for example, Forward Collision Avoidance Assist (FCA) and Automatic Emergency Braking (AEB) systems. These systems can determine the risk of a vehicle colliding with another vehicle or oncoming traffic in a driving situation and apply emergency braking to avoid a collision if the likelihood of a collision is high.

[0039] However, some implementations of FCA systems may have difficulty identifying states (e.g., events) associated with any vehicles other than the vehicle immediately ahead of the (ego) vehicle in the vehicle's field of view (FOV), and due to this inaccurate identification, may not be able to accurately determine brake control timing. Consequently, a collision with the lead vehicle may not be effectively assessed and prevented.

[0040] According to the Society of Automotive Engineers (SAE), the levels of automation of autonomous vehicles can be categorized as follows. At Level 0, the SAE classification standard may correspond to "no automation," in which the autonomous driving system temporarily engages in emergency situations (e.g., automatic emergency braking) and / or only provides warnings (e.g., blind spot warning, lane departure warning, etc.), and the driver is expected to operate the vehicle. At Level 1, the SAE classification standard may correspond to "driver assistance," in which the system performs some driving functions (e.g., steering, acceleration, braking, lane centering, adaptive cruise control, etc.) while the driver partially operates the vehicle in normal operation, and the driver is expected to determine the operating state and / or timing of the system, perform other driving functions, and respond to (e.g., resolve) the emergency situation. At Level 2, the SAE classification standard may correspond to "partial automation," in which the system performs steering, acceleration, and / or braking under the driver's supervision, and the driver is expected to determine the operating state and / or timing of the system, perform other driving functions, and respond to (e.g., resolve) the emergency situation. At Level 3, the SAE classification standard may correspond to "conditional automation," in which the system drives the vehicle under limited conditions (e.g., performs driving functions such as steering, acceleration, and / or braking), but transfers driving control to the driver when required conditions are not met, and the driver is expected to determine the operating state and / or timing of the system and take over control in emergency situations, but does not otherwise operate the vehicle (e.g., steering, acceleration, and / or braking). At Level 4, the SAE classification standard may correspond to "high automation," in which the system performs all driving functions, and the driver is expected to take over control of the vehicle only in emergency situations. At Level 5, the SAE classification standard may correspond to "full automation," in which the system performs all driving functions without any driver assistance (including in emergency situations), and the driver is expected to perform no driving functions other than determining the operating state of the system. While the present invention may apply the SAE classification standard to automated driving classification, other classification methods and / or algorithms may be used in one or more of the configurations described herein. One or more features associated with automated driving control may be activated based on one or more configured automated driving control settings (e.g., based on at least one of the following: automated driving classification, selection of a vehicle automated driving level, etc.).

[0041] Based on one or more of the features described herein (e.g., controlling the vehicle in a safety control mode based on the behavior of a lead vehicle), the operation of the vehicle can be controlled. The 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, acceleration rate control, warning timing control, forward collision warning timing control, etc.).

[0042] For example, one or more auxiliary devices (e.g., engine brakes, exhaust brakes, hydraulic retarder, electric retarder, regenerative brakes, etc.) may also be controlled based on one or more features described herein (e.g., controlling the vehicle of the present invention in a safe control mode based on the behavior of the lead vehicle). For example, one or more communication devices (e.g., modems, network adapters, radio transceivers, antennas, etc., which are capable of communicating via one or more wired or wireless communication protocols, such as Ethernet, Wi-Fi, near field communication (NFC), Bluetooth, long term evolution (LTE), 5G new radio (NR), vehicle-to-everything (V2X), etc.) may also be controlled based on one or more features described herein (e.g., controlling the vehicle of the present invention in a safe control mode based on the behavior of the lead vehicle).

[0043] For example, one or more minimum risk maneuvers (MRMs) may also be controlled based on one or more features described herein (e.g., controlling the vehicle in a safe control mode based on the behavior of the lead vehicle). A minimum risk maneuver (e.g., a minimal risk maneuver, a minimum risk maneuver) may be a maneuver of the vehicle to minimize (e.g., reduce) the risk of collision with surrounding vehicles to achieve a lower (e.g., lowest) risk state. A minimum risk maneuver may be an operation that is activated during autonomous vehicle operation when the driver is unable to respond to an intervention request. During a minimum risk maneuver, one or more processors of the vehicle may control the driving operation of the vehicle for a set time period.

[0044] For example, one or more biased driving maneuvers may also be controlled based on one or more of the features described herein (e.g., controlling the vehicle in a safe control mode based on the behavior of the lead vehicle). The drive control device may perform biased driving control. To perform biased driving, the drive control device may control the vehicle to stay in the lane by maintaining a lateral distance between the center position of the vehicle and the center of the lane. For example, the drive control device may control the vehicle to stay in the lane, but not in the center of the lane.

[0045] The driving control device can identify an offset target lateral distance for an offset driving control. For example, the offset target lateral distance may include an intentionally adjusted lateral distance that the vehicle may aim to maintain from a reference point (such as the center of a lane or another vehicle) during a maneuver such as a lane change. This adjustment can be made to improve vehicle stability, safety, and / or performance in various driving conditions. For example, during a lane change, the driving control system can offset the lateral distance to maintain a safer gap from adjacent vehicles, taking into account factors such as vehicle speed, road conditions, and / or the presence of obstacles.

[0046] For example, one or more sensors (e.g., IMU sensors, cameras, lidars, radars, 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.) may also be controlled based on one or more of the features described herein (e.g., controlling the vehicle in a safety control mode based on the behavior of the lead vehicle).

[0047] Operational controls for autonomous driving of a vehicle may include various driving controls of the vehicle by vehicle control devices (for example, 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 brake assist control, traffic sign recognition control, adaptive headlight control, etc.).

[0048] Figure 1 is a block diagram illustrating an autonomous vehicle.

[0049] refer to Figure 1 , the autonomous driving vehicle 100 may include at least one sensor 110 and a processor 130 .

[0050] Sensors 110 may be installed as one or more sensors on the autonomous vehicle 100. Sensors 110 may be installed on the autonomous vehicle 100 to obtain various sensor information about the environment surrounding the autonomous vehicle 100 while the autonomous vehicle 100 is traveling, and to provide the sensor information to the processor 130, as described below. For ease of explanation, the autonomous vehicle 100 may also be referred to herein as the ego vehicle. The vehicle being actively controlled by the autonomous driving system may be referred to as the ego vehicle, the host vehicle, or the autonomous driving vehicle. The ego vehicle (e.g., the host vehicle, the autonomous driving vehicle, etc.) may be a vehicle equipped with the autonomous driving system. The vehicle in front of the ego vehicle (e.g., in the same lane as the ego vehicle) may be referred to as the leading vehicle, the lead vehicle, or the preceding vehicle. The vehicle behind the ego vehicle (e.g., in the same lane as the ego vehicle) may be referred to as the trailing vehicle, the trailing vehicle, or the following vehicle. A target vehicle may be any vehicle that is near the ego vehicle (e.g., within a threshold distance from the ego vehicle) and is being monitored and / or analyzed by the autonomous driving system. Target vehicles may include, for example, one or more lead vehicles and / or trailing vehicles.

[0051] The sensor information may include various information about other vehicles traveling near the currently traveling autonomous driving vehicle 100. The sensor information may include, for example, the distance between the self-vehicle 100 and the other vehicle, the relative speed of the other vehicle, the lane position of the other vehicle, information about obstacles and traffic lights, etc.

[0052] The sensor 110 may include, for example, a camera, a radar, a lidar, and a global positioning system (GPS). The sensor 110 may obtain at least one of the following through the camera, radar, and lidar: an image of the surrounding environment of the ego vehicle 100, the distance between the ego vehicle 100 and another vehicle, the relative speed of the other vehicle, the position of the other vehicle, obstacles, and traffic lights, etc., and may obtain the current position of the ego vehicle 100 through the GPS. However, the example is not limited thereto.

[0053] The processor 130 may receive at least one sensor information from the sensor 110 mounted as a plurality of sensors on the ego vehicle 100 and may sense other vehicles traveling near the ego vehicle 100 based on the sensor information.

[0054] For example, in a case where a sensed vehicle is traveling ahead of the ego vehicle 100 but is traveling in the same lane as the ego vehicle 100 , the processor 130 may set the sensed other vehicle as a target vehicle.

[0055] Once the target vehicle is set, the processor 130 can collect driving information of the target vehicle using the sensor 110. The driving information may include lateral position information of the target vehicle, lateral speed information of the target vehicle, lateral direction information of the target vehicle, and information about the path of the target vehicle and the path of the ego vehicle 100.

[0056] The processor 130 may compare and analyze the collected driving information and a preset (eg, predetermined) cut-out condition, and may determine whether the cut-out condition is satisfied based on a result of the comparison and analysis.

[0057] The processor 130 may control the ego vehicle 100 to operate in the first safety control mode or the second safety control mode based on the determination result. This will be described in more detail below.

[0058] Figure 2 is a flow chart illustrating a method of controlling an autonomous vehicle.

[0059] refer to Figure 2 , an example method of controlling the driving of the autonomous driving vehicle 100 is as follows.

[0060] In step S110, under the control of the processor 130, the autonomous driving vehicle 100 may determine whether there is a preceding vehicle (e.g., a leading vehicle) in the same lane as the autonomous driving vehicle 100 while traveling. The leading vehicle may be in front of (e.g., directly in front of) the autonomous driving vehicle 100. For example, under the control of the processor 130, the autonomous driving vehicle 100 may receive at least one sensor information from the sensor 110, which is provided as a plurality of sensors in the autonomous driving vehicle 100, and may sense another vehicle traveling in front of the autonomous driving vehicle 100 based on the sensor information.

[0061] For example, in a case where another vehicle is sensed to be traveling ahead of the ego vehicle 100 but in the same lane as the ego vehicle 100 , the autonomous driving vehicle 100 may set the sensed other vehicle as a target vehicle under the control of the processor 130 .

[0062] In step S130, under the control of the processor 130, the autonomous driving vehicle 100 may collect driving information of the set target vehicle, and may compare and determine the collected driving information with the set cut-out conditions.

[0063] The driving information may include lateral position information of the target vehicle, lateral speed information of the target vehicle, lateral direction information of the target vehicle, and information about a path of the target vehicle and a path of the ego vehicle 100 .

[0064] Under the control of the processor 130, the autonomous driving vehicle 100 may compare and analyze the collected driving information and the preset cut-out conditions, and determine whether the cut-out conditions are met based on the results of the comparison and analysis.

[0065] For example, under the control of the processor 130, in steps S131, S133, S135, and S137, the autonomous driving vehicle 100 may calculate, based on the collected driving information, a first mobility index (mobility index 1) based on the lateral position information of the target vehicle, a second mobility index (mobility index 2) based on the lateral velocity information of the target vehicle, a motion index based on the lateral direction information of the target vehicle, and a collision index based on the path of the target vehicle and the path of the ego vehicle 100. This will be described in more detail below.

[0066] In step S150, under the control of the processor 130, the autonomous driving vehicle 100 may compare and analyze each of the calculated first movement index, the calculated second movement index, the calculated motion index, and the calculated collision index relative to a preset cut-out condition.

[0067] In this case, if any one of the calculated first movement index, the calculated second movement index, the calculated motion index, and the calculated collision index does not meet the preset cut-out condition, then under the control of the processor 130, the autonomous driving vehicle 100 may determine that the cut-out condition is not met ("No" in step S150).

[0068] In step S170, if it is determined that the cut-out condition is not satisfied, the autonomous driving vehicle 100, under the control of the processor 130, may predict that the target vehicle is extremely unlikely to cut out suddenly or sharply and that the situation in front of the target vehicle is safe, and may operate in the first safety control mode based on the predicted result. The first safety control mode may be a safety control mode that operates based on a preset brake control time.

[0069] In contrast, if the calculated first movement index, the calculated second movement index, the calculated motion index, and the calculated collision index all meet the preset cut-out conditions, then under the control of the processor 130, the autonomous driving vehicle 100 can determine that the cut-out conditions are met ("Yes" in step S150).

[0070] In step S190, if it is determined that the cut-out condition is met, the autonomous driving vehicle 100, under the control of the processor 130, can predict that the target vehicle is very likely to cut out suddenly or sharply, and that the target vehicle is unsafe due to an unexpected event or dangerous situation in front of the target vehicle, and can operate in the second safety control mode based on the predicted result. The second safety control mode can be a safety control mode that operates by changing the preset brake control time to another brake control time. The brake control time of the second safety control mode can be earlier than that of the first safety control mode (for example, the brake control time associated with the second safety control mode can be shorter than the brake control time associated with the first safety control mode). The second safety control mode may require a faster vehicle reaction (e.g., braking) time than the first safety control mode.

[0071] As described above, under the control of the processor 130 , the autonomous driving vehicle 100 may calculate the behavior of the target vehicle with respect to the cut-out and may change the brake control time.

[0072] In step S210 , under the control of the processor 130 , the autonomous driving vehicle 100 may provide braking control to avoid or mitigate a collision with a target vehicle or an emergency occurring in front of the target vehicle.

[0073] Figure 3 is a flowchart illustrating a method of calculating a first movement index (Movement Index 1) based on lateral position information of a target vehicle.

[0074] refer to Figure 3 , an example method for calculating the first movement index is as follows.

[0075] In step S110, under the control of the processor 130, the autonomous driving vehicle 100 may determine whether there is a preceding vehicle in the same lane ahead of the ego vehicle 100. As it has been described above, this will not be described in detail again.

[0076] In step S131, under the control of the processor 130, the autonomous driving vehicle 100 may collect driving information of a set target vehicle and compare and determine the collected driving information with a preset cut-out condition. The driving information may include lateral position information of the target vehicle.

[0077] In step S131a, the autonomous driving vehicle 100 may set a temporary (temporary) value of the lateral position of the target vehicle as the current lateral position of the target vehicle under the control of the processor 130. In this case, the current lateral position of the target vehicle may be set based on an absolute value (absolute value reference) as a reference.

[0078] In step S131b, under the control of the processor 130, the autonomous driving vehicle 100 can use the collected driving information to track the changed temporary value of the lateral position of the target vehicle in real time, and can compare and analyze the changed temporary value of the lateral position of the target vehicle with the current lateral position of the target vehicle as a set absolute value benchmark.

[0079] If the current lateral position of the target vehicle (as an absolute value reference) is greater than the temporary value of the lateral position of the target vehicle, under the control of the processor 130, the autonomous driving vehicle 100 can determine that the target vehicle is moving in the left / right direction from the center of the self-vehicle 100.

[0080] In step S131c, whenever the current lateral position of the target vehicle (as an absolute value reference) becomes greater than the temporary value of the lateral position of the target vehicle, under the control of the processor 130, the autonomous driving vehicle 100 may count the cut-out determination count (Cnt) of the target vehicle to 1 (+1).

[0081] If the cut-out determination count of the target vehicle becomes greater than a preset threshold determination value (Cth), the autonomous driving vehicle 100 may determine a cut-out situation of the target vehicle from the current lane to the adjacent lane under the control of the processor 130 .

[0082] In step S131d, if the cut-out determination count of the target vehicle is greater than the preset threshold determination value Cth, under the control of the processor 130, the autonomous driving vehicle 100 may determine that the preset cut-out condition is satisfied.

[0083] In step S131e, if the cut-out situation is determined, the autonomous driving vehicle 100 may set the first movement index (movement index 1) to "1 (index A=1)" under the control of the processor 130.

[0084] That is, if the current lateral position of the target vehicle (as an absolute value reference) increases to be greater than the temporary value of the lateral position of the target vehicle compared to the previous value, then under the control of the processor 130, the autonomous driving vehicle 100 can predict that the target vehicle is moving in the left / right direction from the center of the self-vehicle 100.

[0085] Therefore, under the control of the processor 130, the autonomous driving vehicle 100 can continuously monitor this trend, and if the target vehicle continues to move in the left / right direction relative to the center of the self-vehicle 100, and its movement level is higher than a certain level relative to the initial selected point of the target vehicle, it can be determined that the target vehicle is cutting out.

[0086] As described above, under the control of the processor 130, the autonomous driving vehicle 100 can calculate a first movement index based on the lateral position of the target vehicle, and track changes in the relative lateral position of the target vehicle based on the calculated first movement index.

[0087] In addition, if the tracked target vehicle continues to move in a direction gradually away from the center of the self-vehicle 100, the autonomous driving vehicle 100 can determine to cut out under the control of the processor 130.

[0088] Figure 4 is a flowchart illustrating a method of calculating a second movement index (Movement Index 2) based on lateral speed information of a target vehicle.

[0089] refer to Figure 4 , an example method for calculating the second movement index is as follows.

[0090] In step S110, under the control of the processor 130, the autonomous driving vehicle 100 may determine whether there is a preceding vehicle in the same lane ahead of the ego vehicle 100. As it has been described above, this will not be described in detail again.

[0091] Under the control of the processor 130, the autonomous driving vehicle 100 may collect driving information of a set target vehicle and compare and determine the collected driving information with a preset cut-out condition. The driving information may include lateral velocity information of the target vehicle. The lateral velocity information of the target vehicle may include the lateral velocity and lateral acceleration of the target vehicle.

[0092] In step S133 , under the control of the processor 130 , the autonomous driving vehicle 100 may analyze the lateral speed information of the target vehicle by collecting the driving information of the set target vehicle.

[0093] In step S133a, based on the analysis result, if the current lateral speed of the target vehicle is higher than the preset threshold lateral speed (vyth), under the control of the processor 130, the autonomous driving vehicle 100 can predict a sudden or sharp cut-out of the target vehicle.

[0094] In step S133b, if the current lateral acceleration of the target vehicle is higher than the preset threshold lateral acceleration (ayth, B2), and the current lateral speed of the target vehicle is higher than the preset threshold lateral speed (vyth, B1), then under the control of the processor 130, the autonomous driving vehicle 100 can determine that the target vehicle suddenly or sharply cuts out of the current lane to the adjacent lane.

[0095] In step 133c, if a cut-out situation is determined, the autonomous driving vehicle 100 may set the second movement index (movement index 2) to "1 (index B=1)" under the control of the processor 130.

[0096] As described above, if the values ​​of the lateral velocity components (e.g., lateral velocity and lateral acceleration) of the target vehicle in the collected driving information of the target vehicle are higher than a certain level, then under the control of the processor 130, the autonomous driving vehicle 100 can determine that the target vehicle is in the following cut-out situation: the target vehicle suddenly or sharply cuts out from the current lane to the adjacent lane.

[0097] Figure 5 and Figure 6 is a schematic diagram illustrating a method of calculating a motion index based on lateral direction information of a target vehicle.

[0098] refer to Figure 5 and Figure 6 , a method for calculating the motion index.

[0099] For example, using the collected driving information, including the lateral position information, lateral speed information, and relative heading angle information of the target vehicle 200, the autonomous driving vehicle 100 can determine the future direction of movement of the target vehicle 200 under the control of the processor 130, and if it is predicted that the target vehicle 200 will change lanes, it can determine to cut out.

[0100] For example, under the control of the processor 130, the autonomous driving vehicle 100 can analyze the current lateral position information of the target vehicle 200, and if the target vehicle 200 is located on the left side relative to the self-vehicle 100, the sign of the lateral position can be set to positive (+), and if the target vehicle 200 is located on the right side relative to the self-vehicle 100, the sign of the lateral position can be set to negative (-).

[0101] In addition, under the control of the processor 130, the autonomous driving vehicle 100 can analyze the current lateral velocity information of the target vehicle 200, and if the target vehicle 200 is traveling in the left direction relative to the self-vehicle 100, the sign of the lateral velocity can be set to positive (+), and if the target vehicle 200 is traveling in the right direction relative to the self-vehicle 100, the sign of the lateral velocity can be set to negative (-).

[0102] In addition, under the control of the processor 130, the autonomous driving vehicle 100 may analyze the relative heading angle information of the target vehicle 200. The relative heading angle information of the target vehicle 200 may be about the relative heading angle between the self-vehicle 100 and the target vehicle 200.

[0103] For example, under the control of the processor 130, the autonomous driving vehicle 100 can analyze the relative heading angle information of the target vehicle 200, and if the relative heading angle of the target vehicle 200 relative to the self-vehicle 100 is in the left direction, the sign of the heading angle can be set to positive (+), and if the relative heading angle of the target vehicle 200 relative to the self-vehicle 100 is in the right direction, the sign of the heading angle can be set to negative (-).

[0104] like Figure 5 As shown, under the control of the processor 130 , the autonomous driving vehicle 100 may set the first motion index C1 using the set lateral direction information of the target vehicle 200 and the set lateral speed information of the target vehicle 200 .

[0105] For example, under the control of the processor 130, if it is determined that the target vehicle 200 is moving in a direction gradually moving away from the center of the self-vehicle 100 based on the set lateral direction information of the target vehicle 200 and the set lateral speed information of the target vehicle 200, the autonomous driving vehicle 100 can set the sign of the first motion index C1 to positive (+).

[0106] In contrast, under the control of the processor 130, if it is determined that the target vehicle 200 is moving in a direction closer to the center of the self-vehicle 100 based on the set lateral direction information of the target vehicle 200 and the set lateral speed information of the target vehicle 200, the autonomous driving vehicle 100 can set the sign of the first motion index C1 to negative (-).

[0107] In addition, if Figure 6 As shown, under the control of the processor 130 , the autonomous driving vehicle 100 may set the second motion index C2 using the set lateral direction information of the target vehicle 200 and the set relative heading angle information of the target vehicle 200 .

[0108] For example, under the control of the processor 130, if the set lateral direction information of the target vehicle 200 and the relative heading angle information of the target vehicle 200 are used to determine that the target vehicle 200 is turning further to the left than the self-vehicle 100, the autonomous driving vehicle 100 can set the sign of the second motion index C2 to positive (+).

[0109] In contrast, under the control of the processor 130, if the set lateral direction information of the target vehicle 200 and the relative heading angle information of the target vehicle 200 are used to determine that the target vehicle 200 is turning more to the right than the self-vehicle 100, the autonomous driving vehicle 100 can set the sign of the second motion index C2 to negative (-).

[0110] If the first motion index and the second motion index are set as described above, under the control of the processor 130 , the autonomous driving vehicle 100 may set the motion index using the set first motion index and the set second motion index.

[0111] For example, under the control of the processor 130, when the target vehicle 200 changes lanes to the right from its current lane, the autonomous driving vehicle 100 may determine that the sign of the target vehicle 200's lateral position is negative (-) and the sign of the relative heading angle is negative (-) as the lateral position of the target vehicle 200 moves to the right. Therefore, the sign of the product of these two values ​​may be positive (+).

[0112] Furthermore, under the control of the processor 130, when the target vehicle 200 changes lanes to the left from its current lane, the autonomous driving vehicle 100 may determine that the sign of the lateral position of the target vehicle 200 is positive (+) and the sign of the relative heading angle of the target vehicle 200 is positive (+) as the lateral position of the target vehicle 200 moves to the left. Therefore, the sign of the product of these two values ​​may be positive (+).

[0113] That is, if the target vehicle 200 moves in a direction gradually moving away from the center of the ego vehicle 100 , the product of the lateral position of the target vehicle 200 and the relative heading angle value of the target vehicle 200 may be a positive number.

[0114] If, as described above, the product of the set first motion index and the second motion index is positive or plus (+), then under the control of the processor 130, the autonomous driving vehicle 100 can determine the cut-out situation and set the motion index to "1 (index C=1)".

[0115] Figure 7 is a schematic diagram illustrating a method of calculating a collision index based on a target vehicle's path and an ego vehicle's path.

[0116] refer to Figure 7 , a method for calculating the collision index.

[0117] The autonomous driving vehicle 100 can analyze the path of the target vehicle 200 and the path of the self-vehicle 100 in the collected driving information, and if it is predicted based on the results of the analysis that these paths overlap, then under the control of the processor 130, the autonomous driving vehicle 100 can determine a value less than or equal to a preset threshold value and can determine a collision.

[0118] The target intersection time (TTIT) may be defined as the time at which the path of the ego vehicle 100 and the path of the target vehicle 200 intersect each other. For example, the first TTIT (TTIT1st) may be the first time point at which the target vehicle 200 enters the path of the ego vehicle 100, and the second TTIT (TTIT2nd) may be the second time point at which the target vehicle 200 enters the path of the ego vehicle 100.

[0119] like Figure 7 As shown, TTIT1st means that if the target vehicle 200 cuts out of the current lane, because the target vehicle 200 initially exists on the current path, the autonomous driving vehicle 100 can set TTIT1st to "0" under the control of the processor 130.

[0120] In contrast, TTIT2nd means that if the target vehicle 200 cuts out of the current lane and meets the current path on the target vehicle's path for the second time, the autonomous driving vehicle 100 can set TTIT2nd to "d" under the control of the processor 130.

[0121] If, in the set TTIT1st and the set TTIT2nd, the calculated TTIT2nd “d” is less than or equal to the preset threshold value (Tth), the autonomous driving vehicle 100 may determine the collision risk under the control of the processor 130 .

[0122] If the calculated TTIT2nd “d” is less than or equal to the preset threshold value Tth, which is determined to be a collision risk as described above, the autonomous driving vehicle 100 can set the collision index to “1 (index D=1)” under the control of the processor 130 .

[0123] Figure 8A and Figure 8B is a schematic diagram showing an example of operation in the first safety control mode.

[0124] refer to Figure 8A and Figure 8B , under the control of the processor 130, the autonomous driving vehicle 100 can use the first movement index (A), the second movement index (B1, B2), the motion index (C1, C2) and the collision index (D) to determine whether the cut-out condition is met.

[0125] For example, Figure 8A and Figure 8BAs shown, if the first movement index (A), the second movement index (B1, B2), the motion index (C1, C2) and the collision index (D) are all set to 1, then under the control of the processor 130, the autonomous driving vehicle 100 can determine that the cut-out conditions are met, and can set a second safety control mode, which advances the brake control time (i.e., the control time of the driving safety function) to prepare for the sudden cut-out situation of the target vehicle 200.

[0126] Therefore, the autonomous driving vehicle 100 can proactively prepare for similar situations or unexpected events that may occur after the target vehicle 200 cuts out.

[0127] Figure 9A and Figure 9B is a schematic diagram showing an example of operation in the second safety control mode.

[0128] refer to Figure 9A and Figure 9B , under the control of the processor 130, the autonomous driving vehicle 100 can use the first movement index (A), the second movement index (B1, B2), the motion index (C1, C2) and the collision index (D) to determine whether the cut-out condition is met.

[0129] For example, Figure 9A and Figure 9B As shown, if at least one of the first movement index (A), the second movement index (B1, B2), the motion index (C1, C2) and the collision index (D) is set to 0 instead of 1, then under the control of the processor 130, the autonomous driving vehicle 100 can determine that the cut-out condition is not met, and can maintain the first safety control mode operating based on the preset control point of the driving safety function.

[0130] like Figure 9B As shown, when the target vehicle 200 moves to the right in a short time and then moves in a straight line in a biased driving state, instead of the lateral position of the target vehicle 200 continuing to move to the right, the autonomous driving vehicle 100 can determine under the control of the processor 130 that the first movement index (A) does not meet the condition (the first movement index (A) determines whether the target vehicle 200 continues to move away from the center of the self-vehicle 100), and the cut-out of the target vehicle 200 can be uncertain.

[0131] In this case, under the control of the processor 130, the autonomous driving vehicle 100 may operate in the first safety control mode based on an existing control time or a preset control time of a driving safety function.

[0132] Figure 10A and Figure 10B is a schematic diagram illustrating the autonomous driving vehicle 100 operating in a second safety control mode.

[0133] refer to Figure 10A and Figure 10B , under the control of the processor 130, in response to the determination result, the autonomous driving vehicle 100 can be controlled to operate in the first safety control mode or the second safety control mode.

[0134] For example, reference Figure 10A If the autonomous driving vehicle 100 maintains the first safety control mode even if the cut-out condition is met, after the target vehicle 200 cuts out, the autonomous driving vehicle 100 can determine the collision risk level after identifying the newly identified control target, thereby delaying the identification of the suddenly identified control target or delaying the determination of the collision probability of a suddenly appearing obstacle under the control of the processor 130.

[0135] Therefore, since the control time (or timing) is determined with a delay under the control of the processor 130, the autonomous driving vehicle 100 may collide with the control target that is identified with a delay, and thus may not be able to perform control at an appropriate time sufficient to avoid the collision.

[0136] In comparison, reference Figure 10B If the cut-out condition is met, under the control of the processor 130, the autonomous driving vehicle 100 can change the control mode to a second safety control mode, which advances the brake control time (which is the control time of the driving safety function) to prepare for the sudden cut-out of the target vehicle 200.

[0137] In this case, based on the distance between the self vehicle 100 and the newly recognized control target after the sudden cut-out, the brake control time can change the collision warning time point, the first emergency braking time point, and the second emergency braking time point simultaneously or sequentially. However, the example is not limited thereto.

[0138] As described herein, under the control of the processor 130, the autonomous driving vehicle 100 can use information such as the lateral position, lateral velocity, lateral acceleration, relative heading angle, etc. of the target vehicle 200 to determine the sudden cut-out behavior of the target vehicle 200, and based on this, the warning / control time (timing) of the driving safety function of the autonomous driving vehicle 100 can be advanced to prepare for possible accidents, the appearance of pedestrians or animals, or similar situations in front of the target vehicle 200, in order to prepare for such sudden collision risks.

[0139] In addition, under the control of the processor 130, the autonomous driving vehicle 100 can use physical quantity information related to the behavior of the target vehicle 200 to determine the cut-out situation based on multiple factors such as the first movement index (A), the second movement index (B1, B2), the motion index (C1, C2) and the collision index (D), and can double-check it to provide robust commercial driving safety technology.

[0140] Furthermore, under the control of the processor 130, the autonomous vehicle 100 can use sensors such as a front camera, a front radar, and a front-side lidar to analyze the behavior of the preceding vehicle. Based on the analysis results, if the preceding vehicle is likely to suddenly change lanes from the current lane in which the preceding vehicle is currently traveling to an adjacent lane, it can be determined that an unexpected situation, such as an accident, has occurred in front of the preceding vehicle. Therefore, under the control of the processor 130, the autonomous vehicle 100 can change the brake control timing for driving safety in preparation for unexpected events, thereby improving driving stability.

[0141] One or more exemplary embodiments of the present invention described herein can be implemented as computer-readable code on a medium in which a program is recorded. Computer-readable media can include all types of recording devices that store data to be read by a computer system. Computer-readable media can include, for example, hard disk drives (HDDs), solid-state drives (SSDs), silicon disk drives (SDDs), read-only memories (ROMs), random access memories (RAMs), optical disk ROMs (CD-ROMs), magnetic tapes, floppy disks, optical data storage devices, and the like.

[0142] The vehicle of the present invention may include a memory configured to store computer instructions and a processor configured to execute the computer instructions, wherein the processor is configured to: sense a preceding vehicle traveling ahead of the vehicle using a plurality of sensors provided on the vehicle; set the preceding vehicle as a target vehicle based on at least one sensor information obtained through sensing; collect driving information of the target vehicle; determine whether the preceding vehicle is likely to cut out of the lane based on the collected driving information and preset cutting-out conditions; and control the vehicle to operate in a first safety control mode or a second safety control mode in response to the determination result.

[0143] The driving information may include lateral position information of the target vehicle, lateral speed information of the target vehicle, lateral direction information of the target vehicle, and information about a path of the target vehicle and a path of the vehicle.

[0144] The processor can be configured to determine, based on the driving information, a first movement index based on the lateral position information of the target vehicle, a second movement index based on the lateral speed information of the target vehicle, a motion index based on the lateral direction information of the target vehicle, and a collision index based on the path of the target vehicle and the path of the vehicle.

[0145] The processor may be configured to control the vehicle in the second safety control mode when the first movement index, the second movement index, the motion index, and the collision index all satisfy a preset cut-out condition.

[0146] The processor may be configured to control the vehicle in the first safety control mode when at least one of the first movement index, the second movement index, the motion index, or the collision index does not satisfy a preset cut-out condition.

[0147] The processor may be configured to set a brake control time associated with the second safety control mode to be different from a brake control time associated with the first safety control mode.

[0148] The processor may be configured to change a brake control time associated with the second safety control mode to be shorter than a brake control time associated with the first safety control mode.

[0149] The brake control time associated with the second safety control mode may include a collision warning time point, a first emergency braking time point, and a second emergency braking time point, and the processor may be configured to sequentially or simultaneously change the collision warning time point, the first emergency braking time point, and the second emergency braking time point based on the distance between the target vehicle and the control target sensed after the target vehicle cuts out of the lane.

[0150] The processor may be configured to set the preceding vehicle as the target vehicle when the preceding vehicle is traveling in the same lane as the vehicle of the present invention.

[0151] The present invention provides a method for controlling a vehicle, which may include: executing computer instructions stored in a memory by a processor, using multiple sensors arranged at the vehicle to sense a preceding vehicle traveling before the vehicle of the present invention; setting the preceding vehicle as a target vehicle based on at least one sensor information obtained through sensing; collecting driving information of the target vehicle; determining whether the preceding vehicle is likely to cut out of the lane based on the collected driving information and preset cutting-out conditions; and controlling the vehicle to operate in a first safety control mode or a second safety control mode in response to the determination result.

[0152] The autonomous driving vehicle and control method configured as described herein can, under the control of a processor, use sensors to analyze the behavior of a leading vehicle, and when it is analyzed that the leading vehicle may suddenly change from the current lane in which it is currently traveling to an adjacent lane, determine a high probability of an unexpected situation (such as an accident) occurring in front of the leading vehicle, and change the brake control time for driving safety to prepare for the emergency.

[0153] In addition, the autonomous driving vehicle and control method configured as described in this article can, under the control of the processor, predict unexpected or dangerous situations in front of the preceding vehicle; and change the brake control time for driving safety based on the predicted results to improve the driving stability of the autonomous driving vehicle.

[0154] In addition, the autonomous driving vehicle and control method configured as described in this article can, under the control of the processor, predict unexpected or dangerous situations in front of a preceding vehicle traveling ahead of the autonomous driving vehicle; and change the brake control time for driving safety based on the predicted results to improve the reliability of the autonomous driving vehicle.

[0155] Therefore, the above detailed description should not be interpreted as limiting, but is illustrative in all aspects. The scope of the embodiments of the present invention should be determined by reasonable interpretation of the claims, and all changes and modifications within the equivalent scope of the present invention are included in the scope of the present invention.

Claims

1. A vehicle comprising: one or more sensors configured to detect at least one vehicle; a memory configured to store computer-readable instructions; as well as a processor configured to execute the computer-readable instructions, The processor is configured to: setting a leading vehicle among the at least one vehicle as a target vehicle, wherein the leading vehicle is traveling in front of the vehicle; receiving, via the one or more sensors, driving information associated with the target vehicle; Determining the possibility of the target vehicle cutting out of the lane based on the driving information and a predetermined cutting-out condition; and Based on the determined likelihood, the vehicle is controlled to operate in one of a plurality of safety control modes.

2. The vehicle according to claim 1, wherein The driving information indicates at least one of the following: The lateral position of the target vehicle, the lateral speed of the target vehicle, the lateral direction of the target vehicle, the path of the target vehicle, or the path of the vehicle.

3. The vehicle according to claim 2, wherein: The processor is further configured to: determining a first movement index based on the lateral position of the target vehicle; determining a second movement index based on the lateral velocity of the target vehicle; determining a motion index based on the lateral direction of the target vehicle; and A collision index is determined based on the path of the target vehicle and the path of the vehicle.

4. The vehicle according to claim 3, wherein: The plurality of safety control modes include a first safety control mode and a second safety control mode, wherein the second safety control mode requires a faster reaction of the vehicle than the first safety control mode, and wherein the processor is configured to control the vehicle to operate in the safety control mode by: The vehicle is controlled in the second safety control mode based on the first movement index, the second movement index, the sport index, and the collision index satisfying the predetermined cut-out condition.

5. The vehicle according to claim 3, wherein The plurality of safety control modes include a first safety control mode and a second safety control mode, wherein the second safety control mode requires a faster reaction of the vehicle than the first safety control mode, and wherein the processor is configured to control the vehicle to operate in the safety control mode by: The vehicle is controlled in the first safety control mode based on at least one of the first movement index, the second movement index, the sport index, or the collision index not satisfying the predetermined cut-out condition.

6. The vehicle according to claim 4, wherein: A brake control time associated with the second safety control mode is different from a brake control time associated with the first safety control mode.

7. The vehicle according to claim 4, wherein: A brake control time associated with the second safety control mode is shorter than a brake control time associated with the first safety control mode.

8. The vehicle according to claim 4, wherein: The brake control time associated with the second safety control mode includes: Collision warning time, first emergency braking time, and second emergency braking time, and Wherein, the processor is further configured to: The collision warning time, the first emergency braking time, and the second emergency braking time are changed based on the distance between the target vehicle and a control target sensed after the target vehicle cuts out of the lane.

9. The vehicle according to claim 1, wherein The processor is further configured to: Based on the fact that the leading vehicle and the vehicle are traveling in the same lane, the leading vehicle is set as the target vehicle.

10. A method performed by a device of a vehicle, the method comprising: detecting at least one vehicle via one or more sensors of the vehicle; setting a leading vehicle among the at least one vehicle as a target vehicle, wherein the leading vehicle is traveling in front of the vehicle; receiving, via the one or more sensors, driving information associated with the target vehicle; Determining the possibility of the target vehicle cutting out of the lane based on the driving information and a predetermined cutting-out condition; and Based on the determined likelihood, the vehicle is controlled to operate in one of a plurality of safety control modes.

11. The method according to claim 10, wherein: The driving information indicates at least one of the following: The lateral position of the target vehicle, the lateral speed of the target vehicle, the lateral direction of the target vehicle, the path of the target vehicle, or the path of the vehicle.

12. The method according to claim 11, further comprising: determining a first movement index based on the lateral position of the target vehicle; determining a second movement index based on the lateral velocity of the target vehicle; determining a motion index based on the lateral direction of the target vehicle; and A collision index is determined based on the path of the target vehicle and the path of the vehicle.

13. The method according to claim 12, wherein: The plurality of safety control modes include a first safety control mode and a second safety control mode, wherein the second safety control mode requires a faster reaction of the vehicle than the first safety control mode, and wherein controlling the vehicle to operate in the safety control mode includes: The vehicle is controlled in the second safety control mode based on the first movement index, the second movement index, the sport index, and the collision index satisfying the predetermined cut-out condition.

14. The method according to claim 12, wherein: The plurality of safety control modes include a first safety control mode and a second safety control mode, wherein the second safety control mode requires a faster reaction of the vehicle than the first safety control mode, and wherein controlling the vehicle to operate in the safety control mode includes: The vehicle is controlled in the first safety control mode based on at least one of the first movement index, the second movement index, the sport index, or the collision index not satisfying the predetermined cut-out condition.

15. The method according to claim 13, wherein A brake control time associated with the second safety control mode is different from a brake control time associated with the first safety control mode.

16. The method according to claim 13, wherein: A brake control time associated with the second safety control mode is shorter than a brake control time associated with the first safety control mode.

17. The method according to claim 13, wherein: The brake control time associated with the second safety control mode includes: Collision warning time, first emergency braking time, and second emergency braking time, and Wherein, the method further comprises: The collision warning time, the first emergency braking time, and the second emergency braking time are changed based on the distance between the target vehicle and a control target sensed after the target vehicle cuts out of the lane.

18. The method according to claim 10, further comprising: Based on the fact that the leading vehicle and the vehicle are traveling in the same lane, the leading vehicle is set as the target vehicle.

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