Apparatus for controlling autonomous driving and method thereof

Abstract

The present invention relates to an autonomous driving device and method, comprising a communication unit that receives driving information of a vehicle, road information, and object information; a storage unit that stores the received information; and a processor that, when the vehicle is located at a road merging section based on the stored information, selects a vehicle to yield based on the received information and controls the driving of the vehicle based on the driving information of the vehicle to yield.

Description

Technology Field

[0001] The present invention relates to an autonomous driving control device and method, and more specifically, to a technology for controlling the driving of a vehicle by making a judgment and autonomously selecting a vehicle to yield at a road merging section. Background Technology

[0002] Roads can be classified into various types based on their form, scale, and function. Fundamentally, they are generally configured so that a single road branches into multiple roads, or multiple roads merge into a single road. In this context, traffic congestion frequently occurs at road merging sections where multiple roads converge. In particular, this congestion leads to problems such as "cut-ins" (driving into traffic).

[0003] Conventional autonomous vehicles perform longitudinal and lateral control of the vehicle against other vehicles cutting in at road merging sections or objects located close to the vehicle. However, since the vehicle is controlled without predicting other vehicles changing lanes at road merging sections, there is a problem in that the sense of unease is not sufficiently reduced.

[0004] In order to solve the aforementioned problem, there is a need for technology regarding autonomous driving control of vehicles that reduces the sense of unfamiliarity at road merging sections. Prior art literature

[0005] Korean Registered Patent Publication No. 10-2570066 (Published Aug. 23, 2023) The problem to be solved

[0006] An embodiment of the present invention aims to provide an autonomous driving control device and method for selecting a vehicle to yield at a road merging section and controlling the driving of the vehicle.

[0007] The technical problems of the present invention are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art from the description below. means of solving the problem

[0008] An autonomous driving control device according to an embodiment of the present invention may include a communication unit that receives driving information of a vehicle, road information, and object information, a storage unit that stores the received information, and a processor that, when the vehicle is located in a road merging section based on the stored information, selects a vehicle to yield based on the received information and controls the driving of the vehicle based on the driving information of the vehicle to yield.

[0009] In one embodiment, the processor may include a candidate selection module that selects a candidate group of vehicles to be yielded based on the received information, a target selection module that selects a vehicle to be yielded based on driving information of at least one candidate vehicle included in the candidate group, and a driving control module that generates a driving control signal for the vehicle to avoid the vehicle to be yielded.

[0010] In one embodiment, the candidate selection module may not select the candidate vehicle if it is determined that the vehicle is in the first section, and may select the candidate vehicle if it is determined that the vehicle is in the second section.

[0011] In one embodiment, the target selection module may select the vehicle to yield when it is determined that the vehicle is in the third section, and the third section may be the road merging section.

[0012] In one embodiment, the first to third sections may be sections set in the lane where the vehicle is traveling, and may be set based on the location of the road merging section and the location of the candidate vehicle.

[0013] In one embodiment, the target selection module can calculate the relative position of the rear bumper of the candidate vehicle with respect to the front bumper of the vehicle.

[0014] In one embodiment, the target selection module can calculate a first predicted time by predicting the time when the candidate vehicle arrives at the boundary between the second section and the third section, calculate a second predicted time by predicting the time when the vehicle arrives at the boundary, and predict the deceleration of the vehicle in the case where the vehicle yields to the candidate vehicle.

[0015] In one embodiment, the target selection module can determine whether the first prediction time is less than or equal to the second prediction time based on the relative position of the rear bumper of the candidate vehicle with respect to the front bumper of the vehicle when the vehicle is in the second section, and can determine whether the deceleration rate exceeds a threshold value based on the relative position.

[0016] In one embodiment, the target selection module may select the yield target vehicle based on the judgment result and deselect the candidate vehicle.

[0017] In one embodiment, the driving control module can calculate a target speed of the vehicle, a maximum speed of the vehicle, and an optimal speed based on the environment of the vehicle based on the stored information, calculate a following speed at which the vehicle follows the vehicle to be yielded, calculate an average value of acceleration based on the calculated target speed, the maximum speed, the optimal speed, and the following speed, and determine the driving speed of the vehicle based on the calculated average value of acceleration.

[0018] A method for controlling autonomous driving of a vehicle according to another embodiment of the present invention may include the steps of receiving driving information of a vehicle, road information, and object information; storing the received information; selecting a vehicle to yield based on the received information when the vehicle is located at a road merging section based on the stored information; and controlling the driving of the vehicle based on the driving information of the vehicle to yield.

[0019] In another embodiment, the step of selecting the vehicle to yield may include the step of selecting a candidate group of the vehicle to yield based on the received information and the step of selecting the vehicle to yield based on driving information of at least one candidate vehicle included in the candidate group, and the step of controlling the driving of the vehicle may include the step of generating a driving control signal of the vehicle to avoid the vehicle to yield.

[0020] In another embodiment, the step of selecting the candidate group may include the step of not selecting the candidate vehicle when it is determined that the vehicle is in a first section, and the step of selecting the candidate vehicle when it is determined that the vehicle is in a second section.

[0021] In another embodiment, the step of selecting the vehicle to yield may include the step of selecting the vehicle to yield when it is determined that the vehicle is in a third section, and the third section may be the road merging section.

[0022] In another embodiment, the first to third sections may be sections set in the lane where the vehicle is traveling, and may be set based on the location of the road merging section and the location of the candidate vehicle.

[0023] In another embodiment, the step of selecting the vehicle to be yielded may further include the step of calculating the relative position of the rear bumper of the candidate vehicle with respect to the front bumper of the vehicle.

[0024] In another embodiment, the step of selecting the vehicle to be yielded may further include the step of calculating a first predicted time by predicting the time when the candidate vehicle arrives at the boundary between the second section and the third section, the step of calculating a second predicted time by predicting the time when the vehicle arrives at the boundary, and the step of predicting the deceleration of the vehicle in the case where the vehicle yields the candidate vehicle.

[0025] In another embodiment, the step of selecting the vehicle to be yielded may further include, when the vehicle is in the second section, determining whether the first prediction time is less than or equal to the second prediction time based on the relative position of the rear bumper of the candidate vehicle with respect to the front bumper of the vehicle, and determining whether the deceleration rate exceeds a threshold value based on the relative position.

[0026] In another embodiment, the step of selecting the vehicle to be yielded may further include the step of selecting the vehicle to be yielded according to a judgment result and the step of deselecting the candidate vehicle.

[0027] In another embodiment, the step of generating the driving control signal may include: calculating the target speed of the vehicle, the maximum speed of the vehicle, and the optimal speed based on the environment of the vehicle based on the stored information; calculating the following speed at which the vehicle follows the vehicle to be yielded; calculating the average value of acceleration based on the calculated target speed, the maximum speed, the optimal speed, and the following speed; and determining the driving speed of the vehicle based on the calculated average value of acceleration. Effects of the invention

[0028] This technology can reduce the sense of unease associated with autonomous driving and improve reliability by selecting a vehicle to yield to at a road merging section and controlling the vehicle's autonomous driving.

[0029] In addition, this technology can prevent vehicle accidents occurring at road merging sections in advance.

[0030] In addition, various effects that can be identified directly or indirectly through this document may be provided. Brief explanation of the drawing

[0031] Figure 1 is a drawing illustrating an example of a road merging section. FIG. 2 is a block diagram showing a vehicle system including an autonomous driving control device according to one embodiment of the present invention. FIG. 3 is a block diagram showing the configuration of a processor according to one embodiment of the present invention. FIGS. 4a to 4d are drawings illustrating a vehicle traveling through a road merging section according to an embodiment of the present invention. FIG. 5 is a flowchart illustrating an autonomous driving control method according to an embodiment of the present invention. FIG. 6 is a flowchart for explaining in detail an autonomous driving control method according to one embodiment of the present invention. FIG. 7 is a block diagram showing a computing system according to one embodiment of the present invention. Specific details for implementing the invention

[0032] Hereinafter, some embodiments of the present invention will be described in detail with reference to exemplary drawings. It should be noted that in assigning reference numerals to the components of each drawing, the same components are given the same reference numeral whenever possible, even if they are shown in different drawings. Furthermore, in describing the embodiments of the present invention, if it is determined that a detailed description of related known components or functions would hinder understanding of the embodiments of the present invention, such detailed description is omitted.

[0033] In describing the components of the embodiments of the present invention, terms such as first, second, A, B, (a), (b), etc., may be used. These terms are intended merely to distinguish the components from other components, and the essence, order, or sequence of the components is not limited by the terms. Furthermore, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the present invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0034] Hereinafter, embodiments of the present invention will be described in detail with reference to FIGS. 1 to 7.

[0035] Figure 1 is a drawing illustrating an example of a road merging section.

[0036] Referring to Fig. 1, the junction (JC) is a section where traffic congestion occurs. At this time, the cutting in of other vehicles to the driver's vehicle can cause accidents or further congestion.

[0037] Let us assume that there is a road merging section (JC) ahead of the lane (DR) in which the driver's vehicle (V) is traveling. Let us also assume that there are target vehicles (TV1 to TV4) traveling in a different lane from the driver's vehicle (V) and intending to merge into the road merging section (JC).

[0038] To solve the aforementioned problem, the present invention can select a yielding vehicle among the target vehicles (TV1 to TV4). Here, the yielding vehicle is a vehicle capable of passing through the road merging section (JC) before the driver vehicle (V). Furthermore, the present invention can control the autonomous driving of the driver vehicle (V) based on the driving information of the yielding vehicle.

[0039] As described above, we intend to provide an autonomous driving control device that selects a vehicle to yield at a road merging section and controls the driving of the vehicle.

[0040] The autonomous driving control device according to the present invention may be provided inside the driver vehicle (V) or may be provided in an external system that communicates with the driver vehicle (V).

[0041] FIG. 2 is a block diagram showing a vehicle system (1) including an autonomous driving control device (10) according to one embodiment of the present invention, and FIG. 3 is a block diagram showing the configuration of a processor (100) according to one embodiment of the present invention.

[0042] Referring to FIG. 2, the autonomous driving control device (10) may include a processor (100), a storage unit (200), and a communication unit (300).

[0043] In one embodiment, the processor (100) can select a vehicle to yield to when the vehicle is located in a road merging section based on information received through the communication unit (300).

[0044] Afterwards, the processor (100) can control the driving of the vehicle based on the driving information of the vehicle to be yielded.

[0045] In one embodiment, the storage unit (200) can store information received through the communication unit (300) and at least one instruction of the processor (100). Here, the storage unit (200) can store all data performed by the candidate group selection module (110), target selection module (120), and driving control module (130) of the processor (100).

[0046] In one embodiment, the communication unit (300) can receive driving information, road information, and object information of a vehicle. For example, the communication unit (300) can receive driving information from a vehicle speed sensor including a wheel speed sensor, an acceleration sensor, a yaw rate sensor, etc. Additionally, the communication unit (300) can receive road information from a lane recognition sensor, a location module, a navigation system, etc. Additionally, the communication unit (300) can receive driving information of other vehicles located around the vehicle and information of structures, etc. from a camera, a radar, etc.

[0047] As another example, the communication unit (300) can transmit and receive the above-described information to an autonomous driving control system including an engine control system, a braking system, and a steering control system. On the other hand, the communication unit (300) can transmit a driving control signal of the vehicle generated by the processor (100) to the autonomous driving control system.

[0048] The communication unit (300) can perform CAN (Controller Area Network) communication or wired communication. For example, a communication network including a body network, a multimedia network, and a chassis network is configured within the vehicle for the control of various control systems mounted in the vehicle and for communication between various control systems, and each of these separated networks can be connected by a processor (100) to transmit and receive CAN (Controller Area Network) communication messages to and from each other. That is, the communication unit (300) can transmit various information to the vehicle system based on the control signal of the processor (100) and can receive various information from the vehicle system.

[0049] Referring to FIG. 3, the processor (100) may include a candidate selection module (110), a target selection module (120), and a driving control module (130).

[0050] In one embodiment, the candidate selection module (110) can select a candidate group of vehicles to yield based on received information. Here, at least one vehicle included in the candidate group may be a candidate vehicle. For example, the candidate selection module (110) can extract the vehicle type, driving speed, steering action, lane departure distance, etc. of other vehicles based on object information. The candidate selection module (110) can select a candidate vehicle based on the extracted information.

[0051] The candidate selection module (110) can determine whether the vehicle is located in the first to third section based on the received road information.

[0052] In one embodiment, the first to third sections may be sections set in the lane where the vehicle is traveling. The first to third sections may be set based on the location of the road merging section and the location of the candidate vehicle. For example, the first and second sections may be sections located at a predetermined distance from the road merging section. The third section may be the road merging section. Additionally, the range of the first and second sections may be expanded if there is no vehicle scheduled to merge into the road merging section. In other words, the range of the first and second sections may be expanded if there is no candidate vehicle. For example, let us assume that only the vehicle is traveling in the main lane and there are no other vehicles on the merging road scheduled to merge into the road merging section. In this case, the third and second sections may not be set, and the section from the vehicle's current location to the road merging section may be set as the first section.

[0053] The candidate selection module (110) may not select a candidate vehicle if it determines that the vehicle is in the first section. The first section may be a section where the vehicle maintains a constant driving speed and drives safely.

[0054] The candidate selection module (110) can select a candidate vehicle when it is determined that the vehicle is in the second section. For example, the candidate vehicle may be another vehicle located in the set third section. As another example, the candidate vehicle may be another vehicle located in the second section but scheduled to enter the third section sooner than the vehicle. Here, the candidate selection module (110) can determine whether the other vehicle can enter the third section sooner than the vehicle based on the other vehicle's driving speed, etc.

[0055] In one embodiment, the target selection module (120) can select a vehicle to yield when it determines that the vehicle is in the third section. For example, since the vehicle enters the third section, i.e., the road merging section, the target selection module (120) can select a vehicle to yield immediately without selecting a candidate vehicle. Alternatively, when the vehicle is in the second section, the target selection module (120) can select a candidate vehicle. Subsequently, when the vehicle enters the third section, the target selection module (120) can select a vehicle to yield from among the candidate vehicles.

[0056] The target selection module (120) can select a vehicle to yield based on driving information of at least one candidate vehicle. For example, the target selection module (120) can track changes in driving speed, changes in steering motion, etc. of the candidate vehicle. Subsequently, the target selection module (120) can select a vehicle to yield from among the candidate vehicles based on the tracked results.

[0057] The target selection module (120) can calculate the relative position of the rear bumper of a candidate vehicle to the front bumper of a vehicle based on object information. For example, the target selection module (120) can calculate which direction the rear bumper of a candidate vehicle is located in, left or right, based on the position of the front bumper of a vehicle. Additionally, the target selection module (120) can calculate whether the front bumper of a vehicle is in front of the rear bumper of a candidate vehicle or whether the front bumper of a vehicle is behind the rear bumper of a candidate vehicle.

[0058] The target selection module (120) can calculate a first prediction time and a second prediction time. The target selection module (120) can predict the time when the candidate vehicle and the vehicle arrive at the boundary between the second section and the third section. Here, the first prediction time is the time between the current time and the time when the candidate vehicle is predicted to arrive at the boundary. The second prediction time is the time between the current time and the time when the vehicle is predicted to arrive at the boundary plus a predetermined margin. Here, the margin may vary depending on the relative position of the candidate vehicle's rear bumper with respect to the vehicle's front bumper. And the target selection module (120) can calculate a threshold time based on the relative position of the candidate vehicle and the vehicle. Here, the threshold time may vary depending on the relative position of the candidate vehicle's rear bumper with respect to the vehicle's front bumper.

[0059] The target selection module (120) can predict the deceleration of a vehicle in the case where the vehicle yields to a candidate vehicle. For example, the target selection module (120) can predict the deceleration of a vehicle assuming that each of the candidate vehicles is selected as the vehicle to yield to. Subsequently, the target selection module (120) can determine whether the deceleration of the vehicle exceeds a threshold value. Here, the threshold value may vary depending on the relative position of the rear bumper of the candidate vehicle to the front bumper of the vehicle.

[0060] In one embodiment, the target selection module (120) can determine the relative position when the vehicle is in the second section. For example, when the vehicle is in the second section, the target selection module (120) can receive a candidate vehicle from the candidate selection module (110). On the other hand, when the vehicle is in the third section, the target selection module (120) can immediately select a vehicle to yield. Subsequently, the target selection module (120) can determine whether the first prediction time for the candidate vehicle is less than or equal to the second prediction time.

[0061] In one embodiment, the target selection module (120) can determine whether the first prediction time is less than or equal to the second prediction time when the front bumper of the vehicle in the second section is behind the rear bumper of the candidate vehicle. It can also determine whether the first prediction time is less than the threshold time. Furthermore, the target selection module (120) can determine whether the predicted deceleration of the vehicle exceeds the threshold. Furthermore, the target selection module (120) can determine whether the candidate vehicle is driving toward the road merging section. When all of the above conditions are satisfied, the target selection module (120) can select the candidate vehicle as the vehicle to yield. In other words, when all of the above conditions are satisfied, the target selection module (120) predicts that the vehicle will meet the candidate vehicle at the road merging section, and thus can select the candidate vehicle as the vehicle to yield. However, this is merely an example, and the present disclosure is not limited thereto.

[0062] In another embodiment, the target selection module (120) can determine whether the first prediction time is less than or equal to the second prediction time when the front bumper of the vehicle is behind the rear bumper of the candidate vehicle. It can also determine whether the first prediction time is less than the threshold time. Furthermore, the target selection module (120) can determine whether the predicted deceleration of the vehicle exceeds the threshold. And the target selection module (120) can determine whether the candidate vehicle is traveling in a direction different from the road merging section. When all of the above conditions are satisfied, the target selection module (120) can release the selected candidate vehicle. That is, the target selection module (120) can exclude the candidate vehicle from the yield target vehicle. Or the target selection module (120) can release the selected yield target vehicle. In other words, when all of the above conditions are satisfied, the target selection module (120) predicts that the vehicle will not meet the candidate vehicle at the road merging section, so it can release the selected candidate vehicle.

[0063] On the other hand, the target selection module (120) can determine whether the first prediction time is less than or equal to the second prediction time when the front bumper of the vehicle is ahead of the rear bumper of the candidate vehicle. It can also determine whether the first prediction time is less than the threshold time. Furthermore, the target selection module (120) can determine whether the driving speed of the candidate vehicle exceeds the threshold speed. Furthermore, the target selection module (120) can determine whether the candidate vehicle is driving toward the road merging section. When all of the above conditions are satisfied, the target selection module (120) can release the candidate vehicle because the candidate vehicle passes the road merging section before the vehicle. That is, the target selection module (120) can exclude the candidate vehicle from the yield target vehicle. However, this is merely an example, and the present disclosure is not limited thereto.

[0064] In another embodiment, the target selection module (120) can calculate the distance between the candidate vehicle and the lane in which the vehicle is traveling when the front bumper of the vehicle in the third section is behind the rear bumper of the candidate vehicle. For example, the target selection module (120) can determine whether the distance between the candidate vehicle and the lane in which the vehicle is traveling is within a threshold range. The target selection module (120) can also determine whether the candidate vehicle is traveling toward a road merging section. When all of the above conditions are satisfied, the target selection module (120) can select the candidate vehicle as the vehicle to yield. In other words, when all of the above conditions are satisfied, the target selection module (120) predicts that the vehicle will meet the candidate vehicle at the road merging section, and thus can select the candidate vehicle as the vehicle to yield. However, this is merely an example, and the present disclosure is not limited thereto.

[0065] In one embodiment, the driving control module (130) can calculate the vehicle's target speed, the vehicle's maximum speed, and the optimal speed based on the vehicle's environment. Here, the target speed may be a speed set by the vehicle, or a speed set by the vehicle to reach a destination at a predetermined time. The maximum speed may be a road speed limit, or a maximum speed according to the vehicle's autonomous driving system. The optimal speed may be a speed that minimizes the risk of an accident by detecting objects around the vehicle. The driving control module (130) can calculate a following speed at which the vehicle follows a vehicle to which it is to yield.

[0066] The driving control module (130) can calculate an average value of acceleration based on the calculated target speed, maximum speed, optimal speed, and follow speed. For example, the driving control module (130) can calculate an average value of acceleration over one second for each of the target speed, maximum speed, optimal speed, and follow speed. Subsequently, the driving control module (130) can determine the driving speed of the vehicle based on the calculated average value of acceleration. For example, the driving control module (130) can calculate a minimum value among the calculated average values ​​of acceleration. Subsequently, the driving control module (130) can determine the driving speed of the vehicle based on the speed having the minimum value among the target speed, maximum speed, optimal speed, and follow speed. In other words, when the follow speed is the minimum value, the driving control module (130) can determine the driving speed of the vehicle based on the follow speed. And the driving control module (130) can generate a driving control signal based on the determined driving speed.

[0067] At the same time, the driving control module (130) can generate a driving control signal for the vehicle to avoid the vehicle to be yielded. Subsequently, the driving control module (130) can transmit the driving control signal to an engine control system, a braking control system, a steering control system, etc., through the communication unit (300). Accordingly, in a road merging section, the vehicle can control its driving so that the vehicle to be yielded passes before the vehicle.

[0068] As described above, the autonomous driving control device (10) according to the present invention can reduce the sense of unease in autonomous driving and improve reliability by selecting a vehicle to yield to in a road merging section and controlling the autonomous driving of the vehicle. In addition, the autonomous driving control device (10) can prevent vehicle accidents occurring in road merging sections in advance.

[0069] FIGS. 4a to 4d are drawings illustrating a vehicle traveling through a road merging section according to an embodiment of the present invention.

[0070] Referring to FIG. 4a, the first to third sections may be sections set in the lane (DR) where the vehicle (V) is traveling. The first to third sections may be set based on the location of the road junction (JC) and the location of the candidate vehicle or other vehicles (TV1 to TV4). For example, the first and second sections may be sections located at a predetermined distance from the road junction (JC). The third section may be the road junction (JC). Additionally, the range of the first and second sections may be expanded if there are no vehicles scheduled to merge into the road junction (JC).

[0071] The candidate selection module (110) may not select a candidate vehicle if it determines that the vehicle (V) is in the first section. The first section may be a section where the vehicle (V) drives safely while maintaining a constant driving speed.

[0072] Referring to FIG. 4b, the candidate selection module (110) can select candidate vehicles (TV1, TV2) when it is determined that the vehicle (V) is in the second section. For example, candidate vehicle TV1 may be another vehicle located in the set third section. As another example, candidate vehicle TV2 may be another vehicle located in the second section but scheduled to enter the third section sooner than the vehicle (V). Here, the candidate selection module (110) can determine whether the other vehicle can enter the third section sooner than the vehicle based on the driving speed of the other vehicle, etc.

[0073] Subsequently, the target selection module (120) can calculate a first prediction time and a second prediction time when the vehicle (V) is in the second section. The target selection module (120) can predict the time when candidate vehicle TV2 and vehicle (V) arrive at the boundary (B23) between the second section and the third section. Here, the first prediction time is the time between the current time and the time when candidate vehicle TV2 is predicted to arrive at the boundary. The second prediction time is the time between the current time and the time when vehicle (V) is predicted to arrive at the boundary (B23) with a predetermined margin added.

[0074] And the target selection module (120) can calculate a threshold time based on the relative positions of the candidate vehicles (TV1, TV2) and the vehicle (V).

[0075] And the target selection module (120) can predict the deceleration of the vehicle in the case where the vehicle yields to the candidate vehicles (TV1, TV2). For example, the target selection module (120) can predict the deceleration of the vehicle (V) when assuming that each of the candidate vehicles (TV1, TV2) is selected as the vehicle to yield to. Subsequently, the target selection module (120) can determine whether the deceleration of the vehicle (V) exceeds a threshold value.

[0076] The target selection module (120) can determine whether the first prediction time is less than or equal to the second prediction time when the front bumper of the vehicle (V) in the second section is behind the rear bumper of the candidate vehicles (TV1, TV2). It can also determine whether the first prediction time is less than the threshold time. Furthermore, the target selection module (120) can determine whether the predicted deceleration of the vehicle (V) exceeds the threshold. Furthermore, the target selection module (120) can determine whether the candidate vehicles (TV1, TV2) are driving toward the road merging section. When all of the above conditions are satisfied, the target selection module (120) can select the candidate vehicles (TV1, TV2) as the vehicles to yield. In other words, when all of the above conditions are satisfied, the target selection module (120) predicts that the vehicle will meet the candidate vehicles (TV1, TV2) at the road merging section, and thus can select the candidate vehicles (TV1, TV2) as the vehicles to yield. However, this is merely an example, and the present disclosure is not limited thereto.

[0077] Referring to FIG. 4c, the target selection module (120) can select a yielding vehicle (TV1) when it is determined that the vehicle (V) is in the third section. For example, since the vehicle (V) enters the third section, i.e., the road junction section (JC), the target selection module (120) can select the yielding vehicle (TV1) immediately without selecting a candidate vehicle. Alternatively, when the vehicle is in the second section, the target selection module (120) can select candidate vehicles (TV1, TV2). Subsequently, when the vehicle enters the third section, the target selection module (120) can select the yielding vehicle (TV1) from among candidate vehicles TV1 and TV2.

[0078] Referring to FIG. 4d, let us assume that only the vehicle (V) is traveling on the main lane (DR) and that there are no other vehicles on the merging road scheduled to merge into the road merging section (JC). In this case, the third section and the second section may not be set, and the section from the vehicle's current location to the road merging section (JC) may be set as the first section.

[0079] FIG. 5 is a flowchart for explaining an autonomous driving control method according to an embodiment of the present invention. The autonomous driving control method of FIG. 5 may be a method performed by the autonomous driving control device (10) of FIG. 2 and FIG. 3.

[0080] Referring to FIG. 5, in step S500, a candidate group of vehicles to yield can be selected based on received information. For example, the candidate group selection module (110) of FIG. 3 can select a candidate group of vehicles to yield based on received information. Here, at least one vehicle included in the candidate group may be a candidate vehicle. For example, the candidate group selection module (110) can extract the vehicle type, driving speed, steering action, lane departure distance, etc. of other vehicles based on object information. The candidate group selection module (110) can select a candidate vehicle based on the extracted information.

[0081] The candidate selection module (110) can determine whether the vehicle is located in the first to third section based on the received road information.

[0082] The candidate selection module (110) may not select a candidate vehicle if it determines that the vehicle is in the first section. The first section may be a section where the vehicle maintains a constant driving speed and drives safely.

[0083] The candidate selection module (110) can select a candidate vehicle when it is determined that the vehicle is in the second section. For example, the candidate vehicle may be another vehicle located in the set third section. As another example, the candidate vehicle may be another vehicle located in the second section but scheduled to enter the third section sooner than the vehicle. Here, the candidate selection module (110) can determine whether the other vehicle can enter the third section sooner than the vehicle based on the other vehicle's driving speed, etc.

[0084] In step S510, a vehicle to yield may be selected based on driving information of at least one candidate vehicle included in the candidate group. For example, the target selection module (120) of FIG. 3 may select a vehicle to yield when it is determined that the vehicle is in the third section. For example, since the vehicle enters the third section, i.e., the road merging section, the target selection module (120) may select a vehicle to yield immediately without selecting a candidate vehicle. Or, when the vehicle is in the second section, the target selection module (120) may select a candidate vehicle. Subsequently, when the vehicle enters the third section, the target selection module (120) may select a vehicle to yield from among the candidate vehicles.

[0085] The target selection module (120) can select a vehicle to yield based on driving information of at least one candidate vehicle. For example, the target selection module (120) can track changes in driving speed, changes in steering motion, etc. of the candidate vehicle. Subsequently, the target selection module (120) can select a vehicle to yield from among the candidate vehicles based on the tracked results.

[0086] The target selection module (120) can calculate the relative position of the rear bumper of a candidate vehicle to the front bumper of a vehicle based on object information. For example, the target selection module (120) can calculate which direction the rear bumper of a candidate vehicle is located in, left or right, based on the position of the front bumper of a vehicle. Additionally, the target selection module (120) can calculate whether the front bumper of a vehicle is in front of the rear bumper of a candidate vehicle or whether the front bumper of a vehicle is behind the rear bumper of a candidate vehicle.

[0087] In step S520, a driving control signal of the vehicle may be generated to avoid the vehicle to be yielded. For example, the driving control module (130) of FIG. 3 may calculate the vehicle's target speed, the vehicle's maximum speed, and the optimal speed based on the vehicle's environment. The driving control module (130) may also calculate a following speed at which the vehicle follows the vehicle to be yielded.

[0088] The driving control module (130) can calculate an average value of acceleration based on the calculated target speed, maximum speed, optimal speed, and follow speed. For example, the driving control module (130) can calculate an average value of acceleration over one second for each of the target speed, maximum speed, optimal speed, and follow speed. Subsequently, the driving control module (130) can determine the driving speed of the vehicle based on the calculated average value of acceleration. For example, the driving control module (130) can calculate a minimum value among the calculated average values ​​of acceleration. Subsequently, the driving control module (130) can determine the driving speed of the vehicle based on the speed having the minimum value among the target speed, maximum speed, optimal speed, and follow speed. In other words, when the follow speed is the minimum value, the driving control module (130) can determine the driving speed of the vehicle based on the follow speed. And the driving control module (130) can generate a driving control signal based on the determined driving speed.

[0089] At the same time, the driving control module (130) can generate a driving control signal for the vehicle to avoid the vehicle to be yielded. Subsequently, the driving control module (130) can transmit the driving control signal to an engine control system, a braking control system, a steering control system, etc., through the communication unit (300). Accordingly, in a road merging section, the vehicle can control its driving so that the vehicle to be yielded passes before the vehicle.

[0090] FIG. 6 is a flowchart for explaining in detail the autonomous driving control method of FIG. 5 according to one embodiment of the present invention.

[0091] Referring to FIG. 6, in step S600, the driving section of the vehicle can be determined based on the location of the road merging section and the location of the candidate vehicle. Here, the driving section may include a first section to a third section. The first section to the third section may be set based on the location of the road merging section and the location of the candidate vehicle.

[0092] In step S610, it can be determined whether the vehicle is in the second section.

[0093] If the vehicle is in the first section or the third section, the vehicle's driving section can be determined again in step S600.

[0094] In one embodiment, the candidate selection module (110) may not select a candidate vehicle if it determines that the vehicle is in the first section. The first section may be a section where the vehicle drives safely while maintaining a constant driving speed.

[0095] In one embodiment, the target selection module (120) can select a vehicle to yield when it determines that the vehicle is in the third section. For example, since the vehicle enters the third section, i.e., the road merging section, the target selection module (120) can select a vehicle to yield immediately without selecting a candidate vehicle.

[0096] On the other hand, when the vehicle is in the second section, in step S620, the relative position of the rear bumper of the candidate vehicle to the front bumper of the vehicle can be calculated. For example, the target selection module (120) can determine the relative position when the vehicle is in the second section. When the vehicle is in the second section, the target selection module (120) can receive the candidate vehicle from the candidate group selection module (110).

[0097] In step S630, a first prediction time and a second prediction time may be calculated. For example, the target selection module (120) may calculate the first prediction time and the second prediction time. The target selection module (120) may predict the time when the candidate vehicle and the vehicle arrive at the boundary between the second section and the third section. Here, the first prediction time is the time between the current time and the time when the candidate vehicle is predicted to arrive at the boundary. The second prediction time is the time between the current time and the time when the vehicle is predicted to arrive at the boundary plus a predetermined margin. Here, the margin may vary depending on the relative position of the candidate vehicle's rear bumper with respect to the vehicle's front bumper. And the target selection module (120) may calculate a threshold time calculated based on the relative position of the candidate vehicle and the vehicle. Here, the threshold time may vary depending on the relative position of the candidate vehicle's rear bumper with respect to the vehicle's front bumper.

[0098] In step S640, the deceleration of the vehicle can be predicted in the case where the vehicle yields to the candidate vehicle. The target selection module (120) can predict the deceleration of the vehicle in the case where the vehicle yields to the candidate vehicle. For example, the target selection module (120) can predict the deceleration of the vehicle assuming that each of the candidate vehicles is selected as the vehicle to yield to. Subsequently, the target selection module (120) can determine whether the deceleration of the vehicle exceeds a threshold value. Here, the threshold value may vary depending on the relative position of the candidate vehicle's rear bumper to the vehicle's front bumper.

[0099] At step S650, as the vehicle to be yielded is selected, the selection of the candidate vehicle may be deselected.

[0100] In one embodiment, the target selection module (120) can determine whether the first prediction time is less than or equal to the second prediction time when the front bumper of the vehicle in the second section is behind the rear bumper of the candidate vehicle. It can also determine whether the first prediction time is less than the threshold time. Furthermore, the target selection module (120) can determine whether the predicted deceleration of the vehicle exceeds the threshold. And the target selection module (120) can determine whether the candidate vehicle is driving toward the road merging section. When all of the above conditions are satisfied, the target selection module (120) can select the candidate vehicle as the vehicle to yield. In other words, when all of the above conditions are satisfied, the target selection module (120) predicts that the vehicle will meet the candidate vehicle at the road merging section, so the candidate vehicle can be selected as the vehicle to yield.

[0101] In another embodiment, the target selection module (120) can determine whether the first prediction time is less than or equal to the second prediction time when the front bumper of the vehicle is behind the rear bumper of the candidate vehicle. It can also determine whether the first prediction time is less than the threshold time. Furthermore, the target selection module (120) can determine whether the predicted deceleration of the vehicle exceeds the threshold. And the target selection module (120) can determine whether the candidate vehicle is traveling in a direction different from the road merging section. When all of the above conditions are satisfied, the target selection module (120) can release the selected candidate vehicle. That is, the target selection module (120) can exclude the candidate vehicle from the yield target vehicle. Or the target selection module (120) can release the selected yield target vehicle. In other words, when all of the above conditions are satisfied, the target selection module (120) predicts that the vehicle will not meet the candidate vehicle at the road merging section, so it can release the selected candidate vehicle.

[0102] On the other hand, the target selection module (120) can determine whether the first prediction time is less than or equal to the second prediction time when the front bumper of the vehicle is ahead of the rear bumper of the candidate vehicle. It can also determine whether the first prediction time is less than the threshold time. Furthermore, the target selection module (120) can determine whether the driving speed of the candidate vehicle exceeds the threshold speed. And the target selection module (120) can determine whether the candidate vehicle is driving toward the road merging section. When all of the above conditions are satisfied, the target selection module (120) can release the candidate vehicle because the candidate vehicle passes the road merging section before the vehicle. That is, the target selection module (120) can exclude the candidate vehicle from the yield target vehicle.

[0103] As described above, the autonomous driving control method according to the present invention can reduce the sense of unease in autonomous driving and simultaneously improve reliability by selecting a vehicle to yield at a road merging section and controlling the autonomous driving of the vehicle. In addition, the autonomous driving control method can prevent vehicle accidents occurring at road merging sections in advance.

[0104] FIG. 7 is a block diagram showing a computing system according to one embodiment of the present invention.

[0105] Referring to FIG. 7, the computing system (1000) may include at least one processor (1100), memory (1300), user interface input device (1400), user interface output device (1500), storage (1600), and network interface (1700) connected via a bus (1200).

[0106] The processor (1100) may be a semiconductor device that executes processing on instructions stored in a central processing unit (CPU) or memory (1300) and / or storage (1600). The memory (1300) and storage (1600) may include various types of volatile or non-volatile storage media. For example, the memory (1300) may include ROM (Read Only Memory) and RAM (Random Access Memory).

[0107] Accordingly, the steps of the method or algorithm described in connection with the embodiments disclosed in this specification may be directly implemented in hardware, software modules, or a combination of both, executed by the processor (1100). The software modules may reside in storage media such as RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs (i.e., memory (1300) and / or storage (1600)).

[0108] An exemplary storage medium is coupled to a processor (1100), and the processor (1100) can read information from the storage medium and write information to the storage medium. Alternatively, the storage medium may be integrated with the processor (1100). The processor and the storage medium may reside within an application-specific integrated circuit (ASIC). The ASIC may reside within a user terminal. Alternatively, the processor and the storage medium may reside as separate components within the user terminal.

[0109] The user interface input device (1400) may include an input device that receives user input.

[0110] For example, the input device can receive various user inputs from the user to set the functions of the vehicle. For example, the input device may be provided as a tact switch, a joystick, a push switch, a slide switch, a toggle switch, a micro switch, or a touch screen. Additionally, the input device may include a microphone for receiving voice input from the user.

[0111] The user interface output device (1500) may include a display that displays various information related to the driving and / or functions of the vehicle, and a speaker that outputs various sounds related to the driving and / or functions of the vehicle.

[0112] Here, the display may provide a user interface for the user and the vehicle to interact. For example, the display may include a Liquid Crystal Display (LCD) panel and / or a Light Emitting Diode (LED).

[0113] The display can provide various information to the user based on control signals from the processor (1100). For example, the display may be installed in the center fascia, which is the central area of ​​the dashboard inside the vehicle, and the display unit may be a component of the head unit or a component of a navigation device provided separately from the head unit. Here, the head unit can process and output audio and video signals, and it is also possible to perform navigation functions. Therefore, the head unit is also referred to as an AVN (Audio Video Navigation) device.

[0114] For example, the display can show a route guidance screen, that is, a screen necessary to perform navigation functions. In addition, the display can show additional screens necessary to perform audio functions, video functions, or dialing functions.

[0115] The speaker can output the sound necessary to perform navigation functions.

[0116] The network interface (1700) may include a long-distance communication module and / or a short-distance communication module that transmits and receives data with an external device (e.g., server (2), user terminal). For example, the network interface (1700) may refer to a communication module capable of performing wireless internet communication such as Wireless LAN (WLAN), Wireless Broadband (Wibro), WiFi, World Interoperability for Microwave Access (WiMAX), and High Speed ​​Downlink Packet Access (HSDPA).

[0117] For example, the user can input a destination through a user interface input device (1400), and the user interface output device (1500) can provide a path to reach the destination.

[0118] The above description is merely an illustrative explanation of the technical concept of the present invention, and those skilled in the art to which the present invention pertains will be able to make various modifications and variations within the scope of the essential characteristics of the present invention.

[0119] Accordingly, the embodiments disclosed in this invention are intended to illustrate, not limit, the technical concept of the invention, and the scope of the technical concept of the invention is not limited by these embodiments. The scope of protection of this invention shall be interpreted by the claims below, and all technical concepts within an equivalent scope shall be interpreted as being included within the scope of rights of this invention.

Claims

Claim 1 An autonomous driving control device comprising: a communication unit for receiving driving information, road information, and object information of a vehicle; a storage unit for storing the received information; and a processor for selecting a vehicle to yield based on the received information and controlling the driving of the vehicle based on the driving information of the vehicle to yield when the vehicle is located in a road merging section based on the stored information. Claim 2 The autonomous driving control device according to claim 1, wherein the processor comprises: a candidate group selection module that selects a candidate group of vehicles to be yielded based on the received information; a target selection module that selects the vehicles to be yielded based on driving information of at least one candidate vehicle included in the candidate group; and a driving control module that generates a driving control signal of the vehicle to avoid the vehicles to be yielded. Claim 3 An autonomous driving control device according to claim 2, wherein the candidate group selection module does not select the candidate vehicle when it is determined that the vehicle is in a first section, and selects the candidate vehicle when it is determined that the vehicle is in a second section. Claim 4 An autonomous driving control device according to claim 3, wherein the target selection module selects the yield target vehicle when it is determined that the vehicle is in the third section, and the third section is the road merging section. Claim 5 An autonomous driving control device according to claim 4, wherein the first to third sections are sections set in the lane in which the vehicle is traveling, and are set based on the location of the road merging section and the location of the candidate vehicle. Claim 6 An autonomous driving control device according to claim 2, wherein the target selection module calculates the relative position of the rear bumper of the candidate vehicle with respect to the front bumper of the vehicle. Claim 7 An autonomous driving control device according to claim 5, wherein the target selection module calculates a first predicted time by predicting the time when the candidate vehicle arrives at the boundary between the second section and the third section, calculates a second predicted time by predicting the time when the vehicle arrives at the boundary, and predicts the deceleration of the vehicle in the case where the vehicle yields the candidate vehicle. Claim 8 An autonomous driving control device according to claim 7, wherein the target selection module determines whether the first prediction time is less than or equal to the second prediction time based on the relative position of the rear bumper of the candidate vehicle to the front bumper of the vehicle when the vehicle is in the second section, and determines whether the deceleration rate exceeds a threshold value based on the relative position. Claim 9 An autonomous driving control device according to claim 2, wherein the target selection module selects the yield target vehicle according to the judgment result and releases the selection of the candidate vehicle. Claim 10 An autonomous driving control device according to claim 2, wherein the driving control module calculates a target speed of the vehicle, a maximum speed of the vehicle, and an optimal speed based on the environment of the vehicle based on the stored information, calculates a following speed at which the vehicle follows the vehicle to be yielded, calculates an average value of acceleration based on the calculated target speed, the maximum speed, the optimal speed, and the following speed, and determines the driving speed of the vehicle based on the calculated average value of acceleration. Claim 11 A method for controlling autonomous driving, comprising: receiving driving information, road information, and object information of a vehicle; storing the received information; selecting a vehicle to yield based on the received information when the vehicle is located at a road merging section based on the stored information; and controlling the driving of the vehicle based on the driving information of the vehicle to yield. Claim 12 The autonomous driving control method according to claim 11, wherein the step of selecting the vehicle to yield comprises: a step of selecting a candidate group of the vehicle to yield based on the received information; and a step of selecting the vehicle to yield based on driving information of at least one candidate vehicle included in the candidate group; and the step of controlling the driving of the vehicle comprises: a step of generating a driving control signal of the vehicle to avoid the vehicle to yield. Claim 13 The autonomous driving control method of claim 12, wherein the step of selecting the candidate group comprises: a step of not selecting the candidate vehicle when it is determined that the vehicle is located in a first section; and a step of selecting the candidate vehicle when it is determined that the vehicle is in a second section. Claim 14 An autonomous driving control method according to claim 13, wherein the step of selecting the vehicle to yield includes the step of selecting the vehicle to yield when it is determined that the vehicle is in a third section, and wherein the third section is the road merging section. Claim 15 An autonomous driving control method according to claim 14, wherein the first to third sections are sections set in the lane in which the vehicle is traveling, and are set based on the location of the road merging section and the location of the candidate vehicle. Claim 16 The autonomous driving control method of claim 12, wherein the step of selecting the vehicle to be yielded further comprises the step of calculating the relative position of the rear bumper of the candidate vehicle with respect to the front bumper of the vehicle. Claim 17 The autonomous driving control method according to claim 15, wherein the step of selecting the vehicle to yield comprises: a step of calculating a first predicted time by predicting the time when the candidate vehicle arrives at the boundary between the second section and the third section; a step of calculating a second predicted time by predicting the time when the vehicle arrives at the boundary; and a step of predicting the deceleration of the vehicle in the case where the vehicle yields the candidate vehicle. Claim 18 The autonomous driving control method of claim 17, wherein the step of selecting the yield target vehicle further comprises: a step of determining whether the first prediction time is less than or equal to the second prediction time based on the relative position of the rear bumper of the candidate vehicle to the front bumper of the vehicle when the vehicle is in the second section; and a step of determining whether the deceleration rate exceeds a threshold value based on the relative position. Claim 19 The autonomous driving control method of claim 12, wherein the step of selecting the vehicle to yield includes: the step of selecting the vehicle to yield according to a judgment result; and the step of releasing the selection of the candidate vehicle. Claim 20 The method of claim 12, wherein the step of generating the driving control signal comprises: a step of calculating a target speed of the vehicle, a maximum speed of the vehicle, and an optimal speed based on the environment of the vehicle based on the stored information; a step of calculating a following speed at which the vehicle follows the vehicle to be yielded; a step of calculating an average value of acceleration based on the calculated target speed, the maximum speed, the optimal speed, and the following speed; and a step of determining the driving speed of the vehicle based on the calculated average value of acceleration.

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