DEVICE AND METHOD FOR DRIVING CONTROL
Patent Information
- Application Number
- DE102024111149
- Authority / Receiving Office
- DE · DE
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2023-07-31
- Filing Date
- 2024-04-22
- Publication Date
- 2025-02-06
Smart Images
Figure 00000000_0000_ABST
Abstract
Description
Technical FieldThe present disclosure and invention relates to an apparatus and method for controlling motion of a vehicle, and more particularly to compensating for unequal (execution) performance in line recognition.BackgroundA driving control device may execute host vehicle control algorithms and may be divided into partial automation (also "partial autonomous"), conditional automation (also "conditional autonomous"), high automation (also "highly autonomous"), and / or full automation (also "full autonomous"), depending on the degree of automated control thereof.For example, the driving control device (also referred to as an autonomous driving control device) may recognize information (e.g., a traffic line) of a road on which the host vehicle travels by a sensor. For example, the driving control device may identify line recognition results using a component (e.g., a front camera) of the sensor disposed in an area of the host vehicle, and perform driving control (or autonomous driving control) of the host vehicle based on the results.For example, the driving control device may identify a polynomial of a certain degree (e.g., a polynomial of third degree) corresponding to (e.g., associated with / associated with) the line (e.g., a traffic line) based on the information of the road identified using the sensor or the driving information of the host vehicle, or a combination thereof.However, the travel control device may experience circumstances in which the accuracy in a process of deriving results corresponding to the line decreases temporarily based on the above-described line recognition algorithm. For example, a spike phenomenon may occur when at least some of the coefficients of the polynomial corresponding to the line briefly increase or decrease.In addition, since the driving environment in which the host vehicle is traveling may not be uniform, inaccuracies may occur in the recognition results, such as line shakes, which occur when the driving environment changes or when the host vehicle is traveling in a certain driving environment.In addition, due to the effective measurement range of the sensor, a situation may occur in which line detection is inaccurate or impossible.Brief ExplanationThe present disclosure has been made to solve the above-mentioned problems occurring in some embodiments while retaining the advantages achieved by these embodiments.An aspect of the present disclosure (hereinafter also briefly only: disclosure) may selectively provide a compensation algorithm depending on whether information about at least one of a curvature of a road identified by a sensor, or a curvature change rate, or any combination thereof satisfies a predetermined condition.Another aspect of the present disclosure may provide a travel control device that determines that a peak phenomenon (also, for example, "swing phenomenon" or "peak phenomenon") has occurred when a change value of a curvature change rate and a change value of a curvature have opposite signs and the change value of the curvature is identified as being equal to or greater than a critical value.Another aspect of the present disclosure may provide a travel control device that performs travel control by compensating line recognition results for a certain period of time (e.g., 2 seconds) and, after the certain period of time has elapsed, continues to use the line recognition results identified by a sensor.Another aspect of the present disclosure may provide a travel control device that selects, among at least one target point candidate, a target point that generates corrected line information such that a difference between a corrected curvature and a reference curvature has a small value as a target point for a compensation algorithm.Another aspect of the present disclosure may provide a travel control device that efficiently sets at least one value among the coefficients corresponding to (e.g., associated with / belonging to) a line using at least one weight.The technical problems to be solved by the present disclosure are not limited to the above-mentioned problems, and all other technical problems not mentioned here will be clearly understood from the following description by those skilled in the art to which the present disclosure relates.According to one or more exemplary embodiments of the present disclosure, an apparatus may include: a sensor, a memory storing instructions, and a controller operatively / operatively (e.g., electrically and / or communicatively) connected to the sensor and the memory. The instructions, when executed by the controller, may cause the apparatus to: obtain, via the sensor, a line detection result associated with a road on which a vehicle is traveling, determine, based on the line detection result, whether information including at least one of a curvature of the road and / or a curvature change rate of the road satisfies a predetermined condition, and generate calibrated (e.g., corrected, adjusted, adjusted, or the like) line information using an expected curvature change rate based on the information satisfying the predetermined condition. The expected curvature change rate may be determined based on: the curvature change rate and at least one / one of an expected heading angle (e.g., also referred to as heading angle) of the vehicle at a destination point on the road, an expected curvature at the destination point, and / or an expected lateral error at the destination point.The predetermined condition may include a first condition including at least one of an increase in the curvature change rate and a decrease in the curvature and / or a decrease in the curvature change rate and an increase in the curvature, and a second condition including a change in the curvature by more than a threshold.The instructions, when executed by the controller, may further cause the apparatus to: control movement of the vehicle based on the line detection result and the calibrated line information during a time period after a time associated with the information.The instructions, when executed by the controller, may further cause the apparatus to: determine at least one candidate target point (s) the vehicle is expected to reach it (s) within a time period after a time associated with the information, and determine the expected curvature change rate at the target point. The target point may be one of the at least one target point candidate.The instructions, when executed by the controller, may further cause the apparatus to: determine, among the curvatures included in the line recognition result, a reference curvature associated with a second time before the time associated with the information, and determine, among the at least one candidate target point, the target point such that a difference between a calibrated curvature at a time when the predetermined condition is satisfied and the reference curvature is the smallest.The instructions, when executed by the controller, may further cause the apparatus to: determine, based on curvature change rates included in the line recognition result, a first curvature change rate at a time associated with the information, and determine, based on the curvature change rates included in the line recognition result, a second curvature change rate representing a total curvature change rate of the road, and determine, based on at least one of the first curvature change rate, the second curvature change rate, or a weight of each of the first curvature change rate and the second curvature change rate, the expected curvature change rate for a time duration beginning at the time associated with the information.The weight of each of the first curvature change rate and the second curvature change rate may include a first weight of the first curvature change rate. The first weight may decrease during the time period.The instructions, when executed by the controller, may further cause the apparatus to: determine, by the sensor, a travel speed of the vehicle, and determine the destination point based on at least one of the travel speed and / or a predetermined amount of time.According to one or more exemplary embodiments of the present disclosure, a method may include: acquiring, by a controller and by a sensor, a line recognition result associated with a road on which a vehicle travels; determining, by the controller and based on the line recognition result, whether information including at least one of a curvature of the road and / or a curvature change rate of the road satisfies a predetermined condition; and generating, by the controller, calibrated (e.g., corrected, adjusted, adjusted, or the like) line information using an expected curvature change rate. The expected curvature change rate may be determined based on: the curvature change rate and at least one / one of an expected heading angle of the vehicle at a destination point on the road, an expected curvature at the destination point, and / or an expected lateral error at the destination point.The predetermined condition may include a first condition including at least one of an increase in the curvature change rate and a decrease in the curvature and / or a decrease in the curvature change rate and an increase in the curvature, and a second condition including a change in the curvature by more than a threshold.The method may further include: controlling, by the controller and during a time period after a time associated with the information, a movement of the vehicle based on the line detection result and the calibrated line information.The method may further include: determining, by the controller, at least one candidate target point (which) the vehicle is expected to reach it (s) within a time period after a time associated with the information; and determining, by the controller, the expected curvature change rate at the target point. The target point may be one of the at least one target point candidate.The method may further include: determining, by the controller and among the curvatures included in the line recognition result, a reference curvature associated with a second time before the information-related time; and determining, by the controller and among the at least one target point candidate, the target point such that a difference between a calibrated curvature at a time when the predetermined condition is satisfied and the reference curvature is the smallest.The method may further include: determining, by the controller and based on curvature change rates included in the line recognition result, a first curvature change rate at a time associated with the information, and determining, based on the curvature change rates included in the line recognition result, a second curvature change rate representing a total curvature change rate of the road, and determining, by the controller and based on at least one of the first curvature change rate, the second curvature change rate, and / or a weight of each of the first curvature change rate and the second curvature change rate, the expected curvature change rate for a time duration beginning at the time associated with the information.The weight of each of the first curvature change rate and the second curvature change rate may include a first weight of the first curvature change rate. The first weight may decrease during the time period.According to one or more exemplary embodiments of the present disclosure, a computer readable medium stores instructions that, when executed, may cause: acquiring, by a controller and by a sensor, a line detection result associated with a road on which a vehicle is traveling; determining, by the controller and based on the line detection result, whether information including at least one of a curvature of the road and / or a curvature change rate of the road satisfies a predetermined condition; and based on the information satisfying the predetermined condition, generating, by the controller, calibrated (e.g., corrected, adjusted, adjusted, or the like) line information using an expected curvature change rate. The expected curvature change rate may be determined based on: the curvature change rate and at least one / one of an expected heading angle of the vehicle at a destination point on the road, an expected curvature at the destination point, or an expected lateral error at the destination point.The predetermined condition may include a first condition including at least one of an increase in the curvature change rate and a decrease in the curvature and / or a decrease in the curvature change rate and an increase in the curvature, and a second condition including a change in the curvature by more than a threshold.The instructions, when executed, may further cause: the controller to control, by the controller and during a time period after a time associated with the information, a movement of the vehicle based on the line detection result and the calibrated line information.The instructions, when executed, may further cause: the controller to determine at least one candidate target point (s) the vehicle is expected to reach it (s) within a time period after a time associated with the information, and the controller to determine the expected rate of change of curvature at the target point. The target point may be one of the at least one target point candidate.The instructions, when executed, may further cause: determining, by the controller and under curvatures included in the line recognition result, a reference curvature associated with a second time before the information-associated time; and determining, by the controller and under the at least one target point candidate, the target point such that a difference between a calibrated curvature at a time when the predetermined condition is satisfied and the reference curvature is the smallest.Brief Description of the DrawingsThe above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings: FIG. 1 is a block diagram showing the components of a travel control device, FIG. 2 is a conceptual diagram showing results of line recognition performed by a travel control device, FIG. 3 is a conceptual diagram showing results of line recognition performed by a travel control device, FIG. 4 is a conceptual diagram showing results of line recognition performed by a travel control device, FIG. 5 is a conceptual diagram showing results of line recognition performed by a travel control device, FIG. 6 is a conceptual diagram of an operation of a travel control device, FIG. 7 is a table showing a temporal change of a parameter used by a travel control device, FIG. 8 is a conceptual diagram of an operation of a travel control device, FIG. 9 is a conceptual diagram of an operation of a travel control device, FIG. 10 is a conceptual diagram of an operation of a travel control device, FIG. 11 is a flowchart of a travel control method; and FIG. 12 shows a data processing system in connection with a driving control method.In connection with the description of the drawings, the same or similar reference numerals may be used for the same or similar components.Detailed DescriptionHereinafter, one or more exemplary embodiments of the present disclosure will be described in detail with reference to the exemplary drawings. In adding reference numerals to the components of each drawing, it should be noted that an identical or equivalent component is denoted by the identical reference numeral, although they are illustrated in other drawings. Further, in describing the one or more exemplary embodiments of the present disclosure, a detailed description of the related known structure or function will be omitted when it is determined that this impairs understanding of the exemplary embodiment of the present disclosure.In describing components of exemplary embodiments of the present disclosure, the terms "first", "second", "A", "B", "(a)", "(b)", and the like may be used. These terms are used only to distinguish one component from another component, but do not limit the respective components regardless of the order or ranking of the respective components. Unless otherwise defined, all terms used herein, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure pertains. Such terms as defined in a general dictionary are to be construed as having a meaning corresponding to the meaning in the context of the related art, and not to be construed as having an ideal or excessively formal meaning unless clearly defined as such in the present application.Hereinafter, one or more exemplary embodiments of the present disclosure will be described in detail with reference to FIGS. 1 to 12.FIG. 1 is a block diagram showing components of a travel control device.A driving control device (e.g. also a driving control device) 100 may comprise at least one / one of a sensor 110, a memory 120 or a controller 130, or any combination thereof. A configuration of the travel control device 100 illustrated in FIG. 1 is exemplary, and the present disclosure is not limited thereto. For example, the travel control apparatus 100 may include other components not illustrated in FIG. 1 (e.g., at least one of an interface, a communication device, a display device, or a notification device, or any combination thereof).The sensor 110 may acquire (or acquire) various information used regarding the travel of a vehicle (e.g., a host vehicle).For example, the sensor 110 may include at least one sensor including at least one / one of a camera, a radar, or a LiDAR, or any combination thereof. For example, the sensor 110 may include a front camera that recognizes or identifies information about an area in front of the host vehicle.For example, the sensor 110 may identify (herein briefly identify only:) information about at least one / any of a host vehicle driving state, a driving mode, a road, or an area adjacent to the host vehicle (e.g., surrounding around the host vehicle), or any combination thereof.For example, the sensor 110 may identify information about the driving state of the host vehicle, including at least one of an actual (or real-time) driving speed, a driving direction, an acceleration, and / or a deceleration of the host vehicle, or any combination thereof.For example, the sensor 110 may identify information about the road on which the host vehicle is traveling. For example, the controller 130 may identify line recognition results concerning the road based on at least some (e.g., one or more) of the information obtained with the sensor 110. The line recognition results may include, for example, information (e.g., a polynomial) about a virtual line corresponding to the road.At least some of the operations described above as being performed by the sensor 110 may be understood to be performed by the controller 130 using the information obtained by the sensor 110.The memory 120 may store instructions or data. For example, the memory 120 may store one or more instructions that, when executed by the controller 130, enable the travel control device 100 to perform various operations.For example, the memory 120 may be implemented as a chipset with the controller 130. The controller 130 may include at least one of a plurality of communication devices or a modem.The storage 120 may store, for example, various information related to the travel control device 100. For example, the memory 120 may store information about the operation history of the controller 130. For example, the memory 120 may store information regarding conditions and / or operation of components of the host vehicle (e.g., at least one / one of an internal combustion engine (ECU), the sensor 110, or the controller 130, or any combination thereof).The controller 130 may be operatively / operatively (e.g., electrically and / or communicatively) connected to the sensor 110 and / or the memory 120. For example, the controller 130 may control the operation of the sensor 110 and / or the memory 120.For example, the controller 130 may identify the line recognition results concerning the road on which the host vehicle is traveling using the sensor 110. For example, the controller 130 may identify the line recognition results having a formula representing the road using at least some (e.g., one or more) of the information obtained with the sensor 110.For example, the line recognition results may include information about Formula 1 below corresponding to (e.g., associated with / associated with) a line of the road.For example, a y value may be a lateral error (e.g., also lateral error) between the identified host vehicle and the line of the road based on an x value corresponding to a real-time position of the host vehicle.For example, the controller 130 may identify information on a driving situation based on the real-time position of the host vehicle based on the following Formula 2.For example, formulas may be formulas representing lateral error between the host vehicle and the line of the road, heading error, curvature of the road (or line), and curvature change rate of the road, respectively, identified based on the x value corresponding to the real-time position of the host vehicle.For example, the controller 130 may identify a heading angle (e.g., also referred to as heading angle) of the host vehicle, the curvature of the road, and / or the curvature change rate as coefficients of Formula 2, based on a case where the x value is 0.For example, when the x value is 0, the curvature change rate ρmay be the curvature change rate ρminof the road 6 a.When the x value is 0, the curvature ρ of the road may be 2 b, for example.When the x value is 0, the heading error θ H of the host vehicle may be c, for example.For example, if the x value is 0, the host vehicle lateral error e D may be d.For example, the controller 130 may identify a destination corresponding to a control destination and perform driving control according to a scheme for reducing a lateral error and / or a heading error of the host vehicle at the identified destination.For example, the controller 130 may use the sensor 110 to determine the real-time travel speed of the host vehicle, and may determine the destination point using at least one of the real-time travel speed or a preset time (or a look-ahead time (e.g., 0.6 seconds)), or any combination thereof. The preset time may be, for example, a setting value that can be changed by the user. The controller 130 may determine a target point x tp by multiplying a real-time travel speed v x of the host vehicle and a preset time t ah.The controller 130 may determine a formula based on Formula 3 below by substituting a target point x tp into Formula 2 above, and determine new line information to replace at least a portion of the existing line recognition results based on the determined formula.For example, the controller 130 may determine whether information on at least one of the curvature or the curvature change rate of the road, or any combination thereof, included in the line recognition results satisfies a predetermined (hereinafter, briefly: predetermined) condition.As an example, the predetermined condition may include a first condition in which, on the one hand, the curvature change rate increases and the curvature decreases and / or, on the other hand, the curvature change rate decreases and the curvature increases. In other words, the controller 130 may determine that the first condition is satisfied when there is a portion in which the curvature change rate increases and the curvature decreases or a portion in which the curvature change rate decreases and the curvature increases in the result data of acquisition of the information on the road using the sensor 110. That is, the controller 130 may determine that the first condition is satisfied when there is a portion in which change values of the curvature change rate and the curvature have signs opposite to each other.As an example, the predetermined condition may include a second condition in which the curvature varies by a value equal to or greater than a critical value. In other words, the controller 130 may determine that the second condition is satisfied when there is a portion in which the curvature changes temporarily (for example, in a short time, temporarily, temporarily, or the like-hereinafter, only briefly: temporarily) by the value equal to or greater than the predefined critical value in the result data of acquiring the information on the road using the sensor 110.For example, when both the above-described first condition and the second condition are satisfied, the controller 130 may determine that the information on at least one of the curvature or the curvature change rate of the road, or any combination thereof, included in the line recognition results satisfies the predetermined condition.For example, if the information satisfies the predetermined condition (e.g., the first condition and / or the second condition), the controller 130 may generate calibrated (e.g., corrected, adjusted, adjusted, or the like) line information using target information having an expected curvature change rate determined using at least one / one of an expected heading angle of the host vehicle, an expected curvature, or an expected lateral error at the target point on the road, or any combination thereof, as well as the curvature change rate.For example, the calibrated (e.g., corrected) line information may include information that corrects at least some (e.g., one or more) of the initially identified line detection results within a certain time duration range to generate new line detection results compensated from the initially identified line detection results, and that enables a subsection of the initially identified line detection results to be replaced with the new line detection results.For example, the controller 130 may control the travel of the host vehicle based on the line recognition results compensated based on the line information calibrated (e.g., corrected) within a certain period of time (e.g., 2 seconds) from a time point at which the information satisfies the predetermined condition.For example, the controller 130 may identify at least one candidate target point which the host vehicle is expected to reach (e.g., expected) within a certain time duration range (e.g., 0.2 seconds before and 0.2 seconds after a time at which 0.6 seconds have elapsed) after a predetermined time duration (e.g., 0.6 seconds) from the time at which the information satisfies the predetermined condition. For example, the controller 130 may identify the at least one candidate target point including at least one of a target point corresponding to a time point after 0.4 seconds, a target point corresponding to a time point after 0.6 seconds, or a target point corresponding to a time point after 0.8 seconds from the time point at which the information satisfies the predetermined condition, or any combination thereof.As an example, the controller 130 may identify target information at a target point among the at least one target point candidate. For example, the controller 130 may identify, among the curvatures included in the line recognition results, a reference curvature at a second time point, which is a time point immediately before a first time point (or the first time point) at which the information satisfies the predetermined condition, and then select, among the at least one target candidate, a target point that generates the calibrated (e.g., corrected) line information such that a difference between a corrected curvature at the time point at which the predetermined condition is satisfied and the reference curvature is the smallest. In other words, the controller 130 may select the target point that generates the calibrated line information such that the curvatures immediately before and after the occurrence of a peak phenomenon (e.g., also, for example, "swing phenomenon" or "peak phenomenon") are continuously connected to each other.The controller 130 may determine a lateral error and a heading error of the host vehicle, the curvature, and the curvature change rate at the target point x tp based on the above Formula 3.For example, the polynomial coefficients of Formula 3 may be different from the polynomial coefficients of Formula 2. The controller 130 may obtain new line information by identifying the polynomial coefficients of Formula 3 by a simultaneous equation operation and identify a new line portion replacing at least a partial portion of a line portion included in the existing line recognition results based on the new line information.Referring to Formula 4, when the spike phenomenon occurs at a time "k", the controller 130 may set a value a 1( k) for generating the new line information to a value a 0( k-1), and then perform the simultaneous equation operation based on the polynomials of Formula 3. The above value set by the controller 130 may be defined as a HOLD( also referred to as "a Halten").Referring to Formula 5, the controller 130 may identify b1, c1, and d1. For example, the controller 130 according to Formula 5 may determine the three unknowns b 1, c 1, and d 1 based on four equations.Referring to Formula 6, the controller 130 may identify the first curvature change rate coefficient a HOLD at the time when the information satisfies the predetermined condition and a second curvature change rate coefficient a 0( k) included in the line recognition results, among the curvature change rates included in the line recognition results. A first curvature change rate and a second curvature change rate, which will be described later, may mean coefficients of a polynomial corresponding to the curvature change rate.As an example, the first curvature rate of change may correspond to (e.g., correspond to) a curvature rate of change coefficient at a time when the spike phenomenon has occurred. In other words, the first curvature change rate may be a curvature change rate identified by the sensor 110 immediately before the occurrence of the spike phenomenon (or at the time the spike phenomenon occurred).As an example, the second curvature change rate may be a curvature change rate coefficient identified by the entire road sensor 110 having the first curvature change rate. In other words, the first curvature change rate may be a fixed value (or a constant), and the second curvature change rate may be a variable that varies over time.For example, the controller 130 may identify an expected curvature change rate during the determined period of time (e.g., 2 seconds) from when the information satisfies the predetermined condition, using at least one of the first curvature change rate, the second curvature change rate, or a weight of each of the first curvature change rate and the second curvature change rate, or any combination thereof.For example, the controller 130 may determine a first weight "α" associated with the first curvature change rate and a second weight (1-α) associated with the second curvature change rate. For example, the controller 130 may set the first weight "α" corresponding to the first curvature change rate to gradually decrease for the certain period of time (e.g., 2 seconds) from the time point at which the information satisfies the predetermined condition.For example, the controller 130 may determine the first weight "α" from the following formula 7.For example, t Act may be a time at which the existing line information starts to be replaced based on the new line information (or a time at which activation of the line correction logic starts). For example, t Deact may be a time point at which the operation of replacing the existing line information based on the new line information ends (or a time point at which activation of the line correction logic ends). For example, t k may be the "k" time. For example, a time period from t Act to t Deact may be 2 seconds in total, but this is an example, and the present disclosure is not limited thereto.An example in which the controller 130 identifies α(k) may be described in detail in a description of FIG. 7 below.For example, using a value of Formula 8 below, the controller 130 may select a target point to be used in identifying new line results from the at least one target point candidate.Referring to Formula 8, the controller 130 may identify, among the at least one target point candidate, a target point that generates the calibrated (e.g., corrected) line information such that the difference between the calibrated curvature at the time the predetermined condition is satisfied and the reference curvature is the smallest and a target time t tp, which belongs to the identified target point.For example, the controller 130 may identify, as a target point to be used for generating the calibrated line information, a target point at which a difference Δb tp between a first calibrated curvature coefficient b 1,tp( k) corresponding to the curvature calibrated based on the target point at the target time t tp and a reference curvature coefficient b 0( k-1) corresponding to the curvature initially (e.g., originally) obtained with the sensor 110 is the smallest.For example, the controller 130 may use Formula 9 below to determine whether at least some (in particular, one or more, e.g., at least one of the curvature or the curvature change rate, or any combination thereof) of the information of the line recognition results obtained using the sensor 110 satisfy the predetermined condition.Referring to Formula 9, the controller 130 may identify a coefficient b 0, which relates to the curvature, and a coefficient a 0, which relates to the curvature change rate, among the information of the line recognition results initially (e.g., originally) acquired with the sensor 110.For example, a sign of a value obtained by multiplying a difference between the coefficient a 0( k) related to a curvature change rate at time "k" and the coefficient a 0( k-1) related to a curvature change rate at time "k-1" and a difference between a coefficient b 0( k) related to a curvature at time "k" and the coefficient b 0( k-1) related to a curvature at time "k-1" can be identified. For example, when the value obtained by multiplying the differences is a negative number, the controller 130 may determine that the first condition in which the curvature change rate needs to increase and the curvature needs to decrease, or in which the curvature change rate needs to decrease and the curvature needs to increase is satisfied.For example, when the difference between the coefficient b 0( k) related to the curvature at the time "k" and the coefficient b 0( k-1) related to the curvature at the time "k-1" which is a time immediately before the time "k" exceeds a critical coefficient b TH the controller 130 may determine that the second condition in which the curvature needs to change by the value equal to or greater than the critical value is satisfied.The components of the travel control device 100 illustrated in FIG. 1 are exemplary, and the present disclosure is not limited thereto. For example, the travel control apparatus 100 may also include the display device (not illustrated) and / or the notification device (not illustrated).The display device may visually provide the user with a user interface having a travel path (e.g., an intended travel path) of the host vehicle.For example, the display device may provide the user with, in real time, a user interface including information about at least one of the line information generated by the controller 130 from the information initially (e.g., originally) acquired with the sensor 110, or the line information newly generated for at least the subsection of the line based on a correction algorithm, or any (e.g., any combination) thereof.The notification device may include at least one output device. For example, the notifying device may include the output device (e.g., a speaker) provided in at least a portion of the vehicle interior of the host vehicle.The notifying device may output various kinds of sounds to the outside, for example.As an example, the notifier may provide the user with an alert message indicating that at least the portion of the line displayed in real time under the control of the controller 130 is displayed using the correction algorithm.FIG. 2 is a conceptual diagram showing the results of line recognition performed by a travel control device.A driving control device (e.g., driving control device 100 in FIG. 1 ) may generate results (e.g., the third degree polynomial) representing a road on which a host vehicle is driving based on at least some (e.g., one or more) of the information obtained with a sensor (e.g., sensor 110 in FIG. 1 ).Referring to reference numeral 210, the travel control device may identify the third degree polynomial corresponding to (e.g., corresponding to) the road (or line). The third degree polynomial may be a formula corresponding to Formula 1 described above.Referring to reference numeral 250, the travel control device may identify a lateral error y(x tg) with the line of the host vehicle at the destination point x tg by substituting the destination point x tg into a variable (e.g., the range of definition) of the third degree polynomial.FIGS. 3 and 4 are conceptual diagrams showing results of line recognition performed by a travel control device.A driving control device (e.g., driving control device 100 in FIG. 1 ) may identify a curvature change rate of a road on which a host vehicle is traveling, a curvature, a heading angle (or heading error), and / or a lateral error using a sensor (e.g., sensor 110 in FIG. 1 ).In the curves of reference numerals 310, 320, 410 and 420, peak portions (also, for example, "swing portions" or "peak portions") occurring during a process in which the travel control device performs line recognition are illustrated.Referring to reference numeral 310 in FIG. 3, the travel control device may identify the curvature change rate of the road in real time.For example, it can be seen that, in a period between 2100 ms and 2110 ms, a first peak portion 315 has occurred in which the curvature change rate detected by the travel control device using the sensor decreases temporarily.Referring to reference numeral 320 in FIG. 3, the travel control device may identify the curvature change rate of the road in real time.For example, it can be seen that a second peak section 325 has occurred in a period between 2100 ms and 2110 ms, in which the curvature detected by the travel control device using the sensor briefly increases.Referring to reference numeral 410 in FIG. 4, the travel control device may identify the heading angle (or heading error) of the host vehicle on the road in real-time.For example, it can be seen that a third section 415 has occurred in a period between 2100 ms and 2110 ms, in which the heading angle detected by the travel control device using the sensor decreases briefly.Referring to reference numeral 420 in FIG. 4, the travel control device may identify the lateral error with the line of the host vehicle on the road in real time.For example, it can be seen that a fourth peak section 425 has occurred in a period between 2100 ms and 2110 ms, in which the lateral error detected by the travel control device with the aid of the sensor decreases briefly.There is a problem that the line recognition accuracy of the travel control device is reduced due to at least some of the above-described peak portions.FIG. 5 is a conceptual diagram showing the results of line recognition performed by a travel control device.A driving control device (e.g., driving control device 100 in FIG. 1 ) may identify changes in a value "a" corresponding to a curvature change rate (or a curvature change rate coefficient) and a value "b" corresponding to a curvature over time (or a curvature coefficient).Referring to reference numeral 510, the travel control device may set a value "a" to a value a HOLD at a first time 512 when the information obtained with a sensor (e.g., sensor 110 in FIG. 1 ) satisfies a predetermined condition. Alternatively, the travel control device may set a value "a" to the value a HOLD at a time immediately before the first time 512. In this connection, when new line information is generated by setting the value "a" to the value a HOLD it may be difficult to generate appropriate line information because a deviation between a final value "a" 514 at a final time point 516 at which a correction algorithm ends when a certain period of time (e.g., 2 seconds) has elapsed from the first time point 512 and an existing (e.g., actual) value "a" of the line information acquired with the sensor is relatively large.In order to avoid such a gap or jump, the travel control device may identify the curvature change rate (or the curvature change rate coefficient) to be used for generating the new line information, based on the above Formula 6. Accordingly, the travel control device can ensure the continuity between the existing line information and the new line information during the process of generating the new line information.Referring to reference numeral 520, the travel control device may identify target point candidates that a host vehicle is expected to reach within a certain period of time (e.g., a period between a time 0.4 seconds after and a time 0.8 seconds after a first time 522) after a predetermined period of time (e.g., 0.6 seconds) from the first time 522 at which the information obtained with the sensor satisfies the predetermined condition.For example, the travel control device may identify at least one of a first target time point 1 tp1, which belongs to a first target point (e.g., a time point 0.4 seconds after the first time point 522), a second target time point t tp2, which belongs to a second target point (e.g., a time point 0.6 seconds after the first time point 522), or a third target time point t tp3, which belongs to a third target point (e.g., a time point 0.8 seconds after the first time point 522), or any combination thereof.For example, the travel control device may determine a difference between a curvature calibrated at the first time 522 based on each of the at least one candidate target point and a reference curvature at the first time 522 obtained in advance by the sensor. For example, the travel control device may respectively identify first curvature information 532 newly generated based on the first target point, second curvature information 534 newly generated based on the second target point, and third curvature information 536 newly generated based on the third target point during a period from the first time 522 to a second time 524 (e.g., a time at which the correction algorithm ends).For example, the travel control device may determine, based on a curve as indicated by reference numeral 520, that a curvature calibrated based on the second target point belonging to the second target time t tp2 has the smallest difference from the reference curvature, and determine the calibrated line information to be generated based on the second target point.FIG. 6 is a conceptual diagram of an operation of a travel control device.Referring to reference number 610, a driving control device (e.g., driving control device 100 in FIG. 1 ) may identify raw data regarding the road using a sensor (e.g., sensor 110 in FIG. 1 ). The raw data may include, for example, at least one of a curvature change rate or a curvature of a road, or any combination thereof.Referring to reference number 650, the driving control device may generate new line information corresponding to (e.g., belonging to) a particular portion of the road when at least some (e.g., one or more) of the raw data meet a predetermined condition via a correction algorithm.For example, the driving control device may detect, as indicated by 652, an event in which the raw data satisfies the predetermined condition.The predetermined condition may include, for example, a first condition in which the curvature change rate increases and the curvature decreases or in which the curvature change rate decreases and the curvature increases, and a second condition in which the curvature changes by a value equal to or greater than a critical value.For example, the travel control device may identify, according to reference numeral 654, the values of at least some (e.g., one or more) of the coefficients of a polynomial corresponding to (e.g., representing) the road. For example, the driving control device may identify a coefficient corresponding to a / each of at least one / each of the curvature change rate, the curvature, a heading error, or a lateral error of a host vehicle, or any combination thereof.For example, the travel control device may select the destination point according to reference numeral 656. A description of the travel control device that selects a destination point may be replaced with the description of FIG. 1.For example, the driving control device may execute main compensation logic (e.g., calibration logic) as indicated at 658. For example, the driving control device may correct at least a portion of a line generated based on the raw data present based on the at least one newly identified coefficient and the destination point.Referring to reference numeral 620, the driving control device may compensate for at least a portion of the line generated based on the raw data based on at least some (e.g., one or more) of the new line information generated based on the correction algorithm of reference numeral 650. The driving control device may identify at least the subsection of the line based on the compensated line detection results.FIG. 7 is a table showing a temporal change of a parameter used by a travel control device.Referring to FIG. 7, a driving control device (e.g., driving control device 100 in FIG. 1 ) may identify an expected curvature rate of change for at least a portion of a road on which a host vehicle is driving based on at least some (e.g., one or more) of the line detection results obtained with a sensor (e.g., sensor 110 in FIG. 1 ).For example, the travel control device may identify a first curvature change rate at the first time point t Act( e.g., the time point at which the spike phenomenon occurred) at which the information acquired with the sensor satisfies a predetermined condition, and a curvature change rate of the entire road included in the line recognition results among the curvature change rates included in the line recognition results.For example, the travel control device may determine, based on a first curvature change rate and a curvature change rate of the entire road, a first coefficient (e.g., a HOLD), which corresponds to (e.g., belongs to / corresponds to) the first curvature change rate, and a coefficient (e.g., a 0( k)), which corresponds to (e.g., belongs to / corresponds to) the curvature change rate of the entire road.The first coefficient may be, for example, a coefficient corresponding to (e.g., belonging to / corresponding to) a curvature change rate at a point of the road that the host vehicle is expected to reach at the first time point t Act, at which the spike phenomenon has occurred (or at a time point immediately before the spike phenomenon occurred).The coefficient corresponding to the curvature change rate of the entire road may be, for example, a coefficient corresponding to (e.g., belonging to / corresponding to) a curvature change rate at a point of the road that the host vehicle is expected to reach at a time "k". The time "k" may be, for example, a time between the first time t Act and the second time t Deact.For example, the driving control device may determine an expected curvature change rate at the second time t Deact, which is after a certain time period (e.g., 2 seconds) from the first time t Act, at which the spike phenomenon occurred, using at least one of the first curvature change rate, the second curvature change rate, or a weight of the first curvature change rate and the second curvature change rate, or any combination thereof.For example, the travel control device may set the first weight α(k) associated with the first curvature change rate on the basis of a curve illustrated in FIG. 7. For example, the travel control device may determine the first weight α(k) based on Formula 7 described above.As an example, the first weight α(k) may be equal to 1 at the first time t Act( or at a time when "k" t Act ).As an example, the travel control device may set the first weight α(k) to gradually decrease from the first time t Act during a certain period of time (e.g., a period of time to the second time t Deact).As an example, the first weight α(k) may be equal to 0 at the second time t Deact( or at the time "k" t Deact ).The travel control device may operate a compensation algorithm (e.g., calibration algorithm) so that compensation (e.g., calibration) of the spike phenomenon is achieved by a relatively large value by setting the first weight α(k) to the fixed value a HOLD at the first time point t Act, at which the accuracy decreases by a relatively large value when the spike phenomenon starts to occur.The travel control device may use curvature change rate related information that takes into account a ratio of a 0( k) corresponding to the information initially identified by the sensor in a gradually increasing manner from the first time point t Act, at which the spike phenomenon has occurred, and is identified by natural convergence to the a 0( k) value without using the value a HOLD in line recognition at the second time point t Deact, at which the compensation algorithm is finally deactivated. Accordingly, the driving control device can continuously ensure the line recognition results for driving control (e.g., the continuity of the line recognition results).FIG. 8 is a conceptual diagram of an operation of a travel control device.A driving control device (e.g., driving control device 100 in FIG. 1 ) may acquire various information initially used for line recognition via a sensor (e.g., sensor 110 in FIG. 1 ). The travel control device may determine whether at least some (e.g., a curvature and a curvature change rate) of the acquired information satisfy a predetermined condition, and when the predetermined condition is satisfied, may generate new line information for a subsection of a line using a compensation algorithm.Referring to reference numeral 810, the driving control device may acquire information related to curvature according to reference numeral 812 using the sensor.For example, it can be seen that in a section between 2100 ms and 2110 ms, a first peak section (e.g., the first peak section 315 in FIG. 3 ) in which the curvature (e.g., in absolute value) obtained by the travel control device using the sensor temporarily increases has occurred.For example, the driving control device may identify at least one target candidate to apply the peak phenomenon compensation algorithm. As the at least one target candidate, at least one target point candidate that is expected to be reached by the host vehicle within a certain period of time (e.g., a period of time from a time 0.4 seconds after to 0.8 seconds after a first time) after a predetermined period of time (e.g., 0.4 seconds) from the first time at which the spike phenomenon occurred may be identified.For example, the driving control device may generate new line information regarding a curvature as indicated by reference numeral 814 based on information identified based on a first destination point identified based on a first destination time point (e.g., a time point 0.4 seconds after the first time point).For example, the driving control device may generate new line information regarding a curvature as indicated by reference numeral 816 based on information identified based on a second destination point identified based on a second destination time point (e.g., a time point 0.6 seconds after the first time point).For example, the driving control device may generate new line information regarding a curvature as indicated by 818 based on information identified based on a third destination point identified based on a third destination time point (e.g., a time point 0.8 seconds after the first time point).The travel control device may identify a reference curvature (e.g., about -1.4) at a second time point, which is a time point immediately before the first time point, (or at the first time point) among the identified line information, and identify line information having the smallest difference (e.g., smallest deviation) from the reference curvature. Referring to FIG. 8, line information as indicated by reference numeral 816 may be considered to have the smallest difference. The travel control device may generate new line information using the line information as indicated by reference numeral 816.Referring to reference sign 820, the driving control device may acquire information regarding a curvature change rate according to reference sign 822 using the sensor.For example, the travel control device may determine that, in a section between 110 ms and 115 ms, a second peak section (e.g., the second peak section 325 in FIG. 3 ) in which the curvature change rate (e.g., in absolute value) obtained by the travel control device using the sensor temporarily increases has occurred.For example, the driving control device may identify at least one target candidate to apply the peak phenomenon compensation algorithm. As the at least one target candidate, at least one target point candidate that is expected to be reached by the host vehicle within a certain period of time (e.g., a period of time from a time 0.4 seconds after to 0.8 seconds after the first time) after a predetermined period of time (e.g., 0.4 seconds) from the first time when the spike phenomenon has occurred may be identified.For example, the driving control device may generate new line information regarding curvature as indicated by reference numeral 824 based on information identified based on the first destination point identified based on the first destination time point (e.g., time point 0.4 seconds after the first time point).For example, the driving control device may generate new line information regarding curvature as indicated by reference numeral 826 based on information identified based on the second target point identified based on the second target time point (e.g., time point 0.6 seconds after the first time point).For example, the driving control device may generate new line information regarding a curvature as indicated by reference numeral 828 based on information identified based on the third destination point identified based on the third destination time point (e.g., time point 0.8 seconds after the first time point).The travel control device may identify a reference curvature change rate (e.g., about 0.5) at the second time point, which is the time point immediately before the first time point, (or at the first time point) among the identified line information, and identify line information having the smallest difference (e.g., the smallest deviation) from the reference curvature change rate. Referring to FIG. 8, line information at reference numeral 828 may be considered to have the smallest difference. The driving control device may generate new line information using the line information indicated by reference numeral 828.If the at least one target candidate is (are) identified according to reference numeral 810 and reference numeral 820, the driving control device may preferably generate calibrated line information based on a target point (e.g., the second target point) identified via the logic according to reference numeral 810. Accordingly, the travel control device can generate new line information for a partial portion of the line.FIG. 9 is a conceptual diagram of an operation of a travel control device.A driving control device (e.g., driving control device 100 in FIG. 1 ) may generate new line information for a subsection of a line via a compensation algorithm.Referring to reference numerals 910 to 940, the driving control device may identify line recognition results including information (e.g., a curvature according to reference numeral 991) obtained by a sensor. For example, the driving control device may identify at least one of a curvature change rate according to reference numeral 991 in a graph shown in reference numeral 910, a curvature according to reference numeral 991 in a graph shown in reference numeral 920, a heading error according to reference numeral 991 in a graph shown in reference numeral 930, or a lateral error according to reference numeral 991 in a graph shown in reference numeral 940, or any combination thereof.For example, when at least some (in particular, one or more, e.g., the curvature and the curvature change rate) of the acquired information satisfy a predetermined condition, the travel control device may perform correction for a portion of the information by a compensation algorithm.For example, the driving controller may activate the compensation algorithm to identify information corresponding to a curve at reference numeral 992 and generate new line information for the subsection of the line based on the identified information. The travel control device may perform the travel control of the host vehicle based on the new line information.FIG. 10 is a conceptual diagram of an operation of a travel control device.A driving control device (e.g., driving control device 100 in FIG. 1 ) may generate new line information for a subsection of a line using a compensation algorithm.For example, the travel control device may identify a time point 1091 at which a spike phenomenon occurs at a maximum.For example, the driving control device may identify a time 1092 at which the spike phenomenon occurred based on a change in at least some (e.g., one or more) of the information obtained with the sensor.For example, the travel control device may identify information on a destination point (which) is (will) used for generating the new line information.For example, the travel control device may identify a first point 1011 and a second point 1012 corresponding to the destination point of the lines. The first point 1011 and the second point 1012 may be, for example, points respectively associated with a left line and a right line of the road at the destination point.For example, the travel control device may generate the new line information using at least some (e.g., one or more) of the information on the road at the destination point, and control a host vehicle 1001 based on the generated line information.FIG. 11 is a flowchart of a travel control method.According to one implementation, a driving control device (e.g., the driving control device in FIG. 1 ) may perform the operations disclosed in FIG. 11. For example, at least some (e.g., one or more) of components (e.g., the sensor 110, the memory 120, and / or the controller 130 in FIG. 1 ) included in the travel control device may be set to perform the operations illustrated in FIG. 11.The operations S 11110 to S 130 may be performed sequentially, but may not necessarily be performed sequentially. For example, the order of the individual operations may be changed, and at least two operations may be performed in parallel with each other. Moreover, a content corresponding to the above-described content with reference to FIG. 11 or redundant may be briefly described or omitted.The driving control device may identify the line recognition results concerning the road on which the host vehicle travels using the sensor (S 1111).The travel control device may determine whether the information on the curvature or the curvature change rate of the road or any combination thereof satisfies the predetermined condition (S 1120).For example, the travel control device may determine whether the information obtained in S 110 satisfies the first condition in which either the curvature change rate increases and the curvature decreases or the curvature change rate decreases and the curvature increases, and / or the second condition in which the curvature changes by the value equal to or greater than the critical value.For example, when the information about the curvature or the curvature change rate of the road or any combination thereof is identified as satisfying the predetermined condition (e.g., S 1120-Yes), the travel control device may execute step S 1130.For example, when the information about the curvature or the curvature change rate of the road or any combination thereof does not satisfy the predetermined condition (e.g., S 1120-No), the travel control device may repeatedly execute step S1110.The driving controller may generate the calibrated line information using at least one / one of the expected heading angle of the host vehicle, the expected curvature, the expected curvature change rate, or the expected lateral error, or any combination thereof, at the destination point on the road (S 1130).For example, the driving control device may perform the driving control for the host vehicle using at least some (e.g., one or more) of the calibrated line information.FIG. 12 shows a data processing system in connection with a driving control method.Referring to FIG. 12, a data processing system (e.g., a computing system, a computer system, or the like) 1000 includes, to the charging robot, the control server, or the charging method, at least a processor 1100, a memory (memory) 1300, a user interface input device 1400, a user interface output device 1500, a (data) storage device (e.g., a mass storage) 1600, and a network interface 1700, which are connected to each other via a bus 1200.The processor 1100 may be a central processing unit (CPU) or a semiconductor device that performs processing of instructions stored in the memory 1300 and / or the storage 1600. The storage 1300 and the storage 1600 may include, for example, various types of volatile or nonvolatile storage media. For example, the memory 1300 may include a read only memory (ROM) 1310 and a random access memory (RAM) 1320.Thus, operations of the method or algorithm described herein may be directly implemented as hardware or a software module executed by the processor 1100, or a combination thereof. The software module may reside on a storage medium (i.e., the memory 1300 and / or the storage 1600) such as a RAM, a flash memory, a ROM, an EPROM, an EEPROM, a register, a hard disk, a removable disk, and a CD-ROM.The example storage medium is connected to the processor 1100, which may 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 in an application specific integrated circuit (ASIC). The ASIC can be provided in a user terminal. Alternatively, the processor and the storage medium may be present as separate components in the user terminal.The above description is merely for illustrating the technical idea of the present disclosure, and various modifications and variations can be made by those skilled in the art without departing from the essential characteristics of the present disclosure.Therefore, the embodiments disclosed in the present disclosure are not intended to limit the technical idea of the present disclosure but to illustrate the present disclosure, and the scope of the technical idea of the present disclosure is not limited by the embodiments. The scope of the present disclosure should be construed as being covered by the scope of the appended claims, and all technical ideas that fall within the scope of the claims should be understood as being included in the scope of the present disclosure.Effects of the apparatus and the method for controlling driving according to the present disclosure are as follows.One or more embodiments of the present disclosure may provide a more adaptive and more comfortable travel experience to the user by ensuring continuity before and after the occurrence of the spike phenomenon and executing the compensation algorithm on the polynomial curve corresponding to the line.Moreover, according to the present disclosure, the travel control device identifying the line with the polynomial for travel control can provide the line recognition results with high accuracy by recognizing that the spike phenomenon occurs in some of the coefficients of the polynomial and suitably compensating this.In addition, according to the present disclosure, when the problem such as the decrease in the accuracy of the line recognition results occurs, at least some of the line recognition results are calibrated by the correction algorithm without cancelling the travel control to lower a frequency of the control cancellation (e.g., a handover of the travel control to the driver), so that a travel control function with improved marketability can be provided.In addition, various effects that directly or indirectly result from the present document may be provided.Although the present disclosure has been described with reference to exemplary embodiments and the accompanying drawings, the present disclosure is not limited thereto, but can be modified and modified in various ways by those skilled in the art to which the present disclosure pertains without departing from the spirit and scope of the present disclosure as claimed in the following claims.
Claims
An apparatus (100) comprising: a sensor (110); a memory (120) storing instructions; and a controller (130) operatively connected to the sensor (110) and the memory (120), wherein the instructions, when executed by the controller (130), cause the apparatus (100) to: acquire, by the sensor (110), a line recognition result associated with a road on which a vehicle travels, based on the line recognition result, whether information including at least one of a curvature of the road and / or a curvature change rate of the road satisfies a predetermined condition, and generate calibrated line information using an expected curvature change rate based on the information satisfying the predetermined condition, wherein the expected curvature change rate is determined based on: the curvature change rate, and at least one / one of an expected heading angle of the vehicle at a destination point on the road, an expected curvature at the destination point and / or an expected lateral error at the destination point.The apparatus (100) of claim 1, wherein the predetermined condition comprises: a first condition comprising one of: an increase in the curvature rate of change and a decrease in the curvature, or a decrease in the curvature rate of change and an increase in the curvature, and a second condition comprising a change in the curvature by more than a threshold.The apparatus (100) of claim 1 or 2, wherein the instructions, when executed by the controller (130), further cause the apparatus (100) to: control a movement of the vehicle based on the line detection result and the calibrated line information during a time period after a time associated with the information.The apparatus (100) of any one of claims 1 to 3, wherein the instructions, when executed by the controller (130), further cause the apparatus (100) to: determine at least one candidate target point that the vehicle is expected to reach within a time period after a time associated with the information, and determine the expected curvature change rate at the target point, wherein the target point is one of the at least one candidate target point.The apparatus (100) according to claim 4, wherein the instructions, when executed by the controller (130), further cause the apparatus (100) to: determine, among the curvatures included in the line recognition result, a reference curvature that is related to a second time before the time related to the information, and determine, among the at least one candidate target point, the target point such that a difference between a calibrated curvature at a time when the predetermined condition is satisfied and the reference curvature is the smallest.The apparatus (100) of any one of claims 1 to 5, wherein the instructions, when executed by the controller (130), further cause the apparatus (100) to: determine, based on curvature change rates included in the line recognition result, a first curvature change rate at a time associated with the information, and determine, based on the curvature change rates included in the line recognition result, a second curvature change rate representing a total curvature change rate of the road, determine, based on at least one of the first curvature change rate, the second curvature change rate, or a weight of each of the first curvature change rate and the second curvature change rate, the expected curvature change rate for a time duration starting at the time associated with the information.The apparatus (100) of claim 6, wherein the weight of each of the first curvature rate of change and the second curvature rate of change comprises a first weight of the first curvature rate of change, the first weight decreasing during the time period.The apparatus (100) of any one of claims 1 to 7, wherein the instructions, when executed by the controller (130), further cause the apparatus (100) to: determine, by the sensor (110), a travel speed of the vehicle, and determine the destination point based on at least one of the travel speed and / or a predetermined time duration.A method, comprising: acquiring (S110), by a controller (130) and by a sensor (110), a line recognition result associated with a road on which a vehicle travels; determining (S120), by the controller (130), and based on the line recognition result, whether information comprising at least one of a curvature of the road and / or a curvature change rate of the road satisfies a predetermined condition; and based on the information satisfying the predetermined condition, generating (S1130), by the controller (130), calibrated line information using an expected curvature change rate, wherein the expected curvature change rate is determined based on: the curvature change rate, and at least one / one of an expected heading angle of the vehicle at a destination point on the road, an expected curvature at the target point and / or an expected lateral error at the target point.The method of claim 9, wherein the predetermined condition comprises: a first condition comprising one of: an increase in the curvature rate of change and a decrease in the curvature, or a decrease in the curvature rate of change and an increase in the curvature, and a second condition comprising a change in the curvature by more than a threshold.The method of claim 9 or 10, further comprising: controlling, by the controller (130) and during a time period after a time associated with the information, a movement of the vehicle based on the line detection result and the calibrated line information.The method according to any one of claims 9 to 11, further comprising: determining, by the controller (130), at least one target point candidate that the vehicle is expected to reach within a time period after a time associated with the information, and determining, by the controller (130), the expected curvature change rate at the target point, wherein the target point is one of the at least one target point candidate.The method according to claim 12, further comprising: determining, by the controller (130) and among the curvatures included in the line recognition result, a reference curvature associated with a second time before the information-associated time; and determining, by the controller (130) and among the at least one target point candidate, the target point such that a difference between a calibrated curvature at a time point at which the predetermined condition is satisfied and the reference curvature is the smallest.The method according to any one of claims 9 to 13, further comprising: determining, by the controller (130) and based on curvature change rates included in the line recognition result, a first curvature change rate at a time associated with the information, and determining, based on the curvature change rates included in the line recognition result, a second curvature change rate representing a total curvature change rate of the road, determining, by the controller (130) and based on at least one of the first curvature change rate, the second curvature change rate, and / or a weight of each of the first curvature change rate and the second curvature change rate, the expected curvature change rate for a time duration starting at the time associated with the information.The method of claim 14, wherein the weight of each of the first curvature rate of change and the second curvature rate of change comprises a first weight of the first curvature rate of change, the first weight decreasing during the time period.A computer readable medium storing instructions that, when executed, cause: obtaining, by a controller (130) and by a sensor (110), a line detection result associated with a road on which a vehicle is traveling; determining, by the controller (130), and based on the line detection result, whether information including at least one of a curvature of the road and / or a curvature change rate of the road satisfies a predetermined condition; and based on the information satisfying the predetermined condition, generating, by the controller (130), calibrated line information using an expected curvature change rate, wherein the expected curvature change rate is determined based on: the curvature change rate, and at least one / one of an expected heading angle of the vehicle at a destination point on the road, an expected curvature at the target point or an expected lateral error at the target point.The computer readable medium of claim 16, wherein the predetermined condition comprises: a first condition comprising one of: an increase in the curvature rate of change and a decrease in the curvature, or a decrease in the curvature rate of change and an increase in the curvature, and a second condition comprising a change in the curvature by more than a threshold.The computer readable medium of claim 16 or 17, wherein the instructions, when executed, further cause: the controller (130), and during a time period after a time associated with the information, to control a movement of the vehicle based on the line detection result and the calibrated line information.The computer readable medium of any one of claims 16 to 18, wherein the instructions, when executed, further cause: the controller (130) to determine at least one candidate target point that the vehicle is expected to reach within a time period after a time associated with the information, and the controller (130) to determine the expected curvature change rate at the target point, wherein the target point is one of the at least one candidate target point.The computer readable medium of claim 19, wherein the instructions, when executed, further cause: determining, by the controller (130) and under curvatures included in the line recognition result, a reference curvature associated with a second time before the information-associated time; and determining, by the controller (130) and under the at least one target point candidate, the target point such that a difference between a calibrated curvature at a time when the predetermined condition is satisfied and the reference curvature is the smallest.