Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Applicant’s amendment filed on November 28, 2025 is acknowledged. Currently claims 1-20 are pending. Claims 1, 2, and 18 has been amended.
Response to Arguments
Applicant’s arguments, filed November 28, 2025, with respect to the rejections of claims 1-20 under 35 U.S.C. 102(a) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Sakai in view of Han.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable of Sakai et al., US 20240273920 A1, (hereinafter “Sakai”) in view of Han et al., US 20250146833 A1, (hereinafter “Han”).
Regarding claim 1, Sakai teaches a processor-implemented method with lane line determination ([0037] “The driving assistance device 100 includes, for example, a CPU and a GPU which are arithmetic devices, and a RAM and a ROM which are storage devices, and can realize these functions by loading a predetermined program stored in the ROM onto the RAM and executing the program in the CPU.”), the method comprising:
determining, from an input image, road feature information comprising a surrounding object comprising , ([0094] “The map information 400 may be either a high-precision map in which information such as a width of a road, a lane marking, connection between lanes, and the like is described in detail, or a navigation map including intersection information and the like. By using lane marking information registered in the map information 400 and information such as the number of lanes and a road width, it is possible to more accurately determine a lane marking type, estimate a lane marking branch point, and integrate lane marking information.” wherein road feature information is the map information);
matching the road feature information to lane lines of a driving road on which a vehicle is driving ([0094] “The map information 400 is used for processing of the lane marking type candidate estimation unit 210, the lane marking type branch point estimation unit 230, and the lane marking information integration unit 260 in FIG. 24.” wherein the road feature information is the map information) ([0057] “Next, in S212, the lane marking type candidate estimation unit 210 collates a graph shape obtained by plotting the luminance of the lane marking of the road surface for each sampling interval extracted in S211 with the model having the lane marking shape obtained by modeling the luminance in advance by using a known method such as template matching to obtain the matching degree.”);
detecting whether there is a change in a road structure based on a result of the matching, wherein the change in the road structure comprises any one or any combination of any two or more of a change of the lane line, a loss of the lane line, and a change of a road sign ([0057] “For example, in the case of a single white line as illustrated in FIG. 7(a), a lane marking shape model appears every cycle. In a case of a zebra pattern as illustrated in FIG. 7(b), a lane marking shape model in which a position and a thickness of a peak change appears.” wherein a change in a road structure is indicated by zebra pattern) ([0042] “In a case where a plurality of lane marking types are assigned to one integrated lane marking as a result of integration of lane markings in the lane marking type integration unit 220, the lane marking type branch point estimation unit 230 detects a point at which a lane marking type changes. A point at which a lane marking type changes may be detected by using a ratio of lane marking types included in the integrated lane marking. In a case where a change point at which the lane marking type changes is found, a region of the lane marking before and after the change point is divided into regions, and lane marking types are assigned to the regions after division.” wherein a change or a loss of the lane line is the change point at which the lane marking type changes are found); and
based on whether the change in the road structure is detected, determining lane line information of the driving road by using information on the surrounding object ([0040] “For the estimation of the lane marking type, a known method, for example, pattern matching using a pattern of the lane marking type, or a lane marking model in which periodicity for each lane marking type is modeled may be used. The lane marking type candidate estimation unit 210 outputs the lane marking type determination result to the lane marking type integration unit 220.” wherein lane line information is lane marking type) ([0096] “Next, in S222′, the lane marking type candidate estimation unit 210 reads surrounding information of the own vehicle from the map information 400. The surrounding information is limited by the map information 400 to be used.” wherein information on the surrounding object is the surrounding information).
Sakai does not specifically disclose a surrounding vehicle and that the surrounding vehicle and the lane line are real-time detection from the input image.
However, Han teaches a surrounding vehicle and that the surrounding vehicle and the lane line are real-time detection from the input image ([0046] “An intelligent driving system of the host vehicle can process the image data of lane lines and the image data of surrounding vehicles to obtain lane line information such as a lane line lateral position, a lane line slope, a lane line curvature, a lane line effective length and a lane line confidence, and vehicle information about the surrounding vehicles such as a vehicle confidence.”) ([0046] “Moreover, it does not need a lot of other vehicle information to be collected and calculated, which improves the real time of the host vehicle congestion sensing, promotes the host vehicle to change lanes in time, and avoids missing the opportunity of lane change.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the lane line detection method of Sakai to a surrounding vehicle wherein information can be detected real-time of Han to improve the accuracy and speed of the vehicle’s ability to detect road feature information.
Regarding claim 2, Sakai in view of Han teaches the method of claim 1, wherein the determining the road feature information comprises any one or any combination of any two or more of:
extracting features of a road surface comprising the road surface marking (Sakai - [0051] “Next, in S202, the lane marking detection unit 200 performs filtering to remove noise from the lane marking candidates extracted in S201. This is because in a case where a line segment is extracted by using a method such as an LSD, not only a crack of a road surface and a shadow of a utility pole or a building but also a road marking is extracted as illustrated in FIG. 3(b).”);
determining a probability value of each type of the features of the road surface; and
determining either one or both of a position and a speed of the surrounding object comprising the surrounding vehicle that is driving on the driving road (Sakai - [0095] “First, in S221′, the lane marking type candidate estimation unit 210 calculates which position on the map a position of the own vehicle corresponds to in order to read the map information 400. For example, the position of the own vehicle on the map is calculated by using latitude, longitude, and altitude acquired by a GNSS, and surrounding information of the own vehicle is read from the map information 400.”).
The motivation for combining Sakai and Han is the same motivation as used for claim 1.
Regarding claim 3, Sakai in view of Han teaches the method of claim 1, wherein the matching comprises:
determining a lane template corresponding to the driving road (Sakai - [0040] “For the estimation of the lane marking type, a known method, for example, pattern matching using a pattern of the lane marking type, or a lane marking model in which periodicity for each lane marking type is modeled may be used.” wherein a lane template is a lane marking model);
determining a matching score by matching the lane template to the road feature information (Sakai - [0057] “Next, in S212, the lane marking type candidate estimation unit 210 collates a graph shape obtained by plotting the luminance of the lane marking of the road surface for each sampling interval extracted in S211 with the model having the lane marking shape obtained by modeling the luminance in advance by using a known method such as template matching to obtain the matching degree.” wherein a matching score is the matching degree and the lane template is the lane marking shape model); and
determining a number of candidate lane lines comprised in the driving road and spacing information between the candidate lane lines, based on the matching score (Sakai - [0050] “First, in S201, the lane marking detection unit 200 extracts a line segment that is a lane marking candidate from acquired data. Lane marking candidates include not only general lane markings including a white line and an orange line but also lane markings including Botts' Dots, cat's eyes, and the like.” wherein a number of candidate lane lines is lane marking candidates) (Sakai - [0053] “Next, in S204, the lane marking detection unit 200 extracts a lane marking from the lane marking candidates. Here, the lane marking is a line having a constant width. FIG. 5 illustrates an example of a method of detecting a lane marking having a constant width.” wherein spacing information is constant width) (Sakai - [0057] “Next, in S212, the lane marking type candidate estimation unit 210 collates a graph shape obtained by plotting the luminance of the lane marking of the road surface for each sampling interval extracted in S211 with the model having the lane marking shape obtained by modeling the luminance in advance by using a known method such as template matching to obtain the matching degree.” wherein a matching score is the matching degree).
The motivation for combining Sakai and Han is the same motivation as used for claim 1.
Regarding claim 4, Sakai in view of Han teaches the method of claim 3, wherein the determining the lane template comprises either one or both of:
determining the lane template based on a curvature range of the candidate lane lines comprised in the driving road (Sakai - [0040] “For the estimation of the lane marking type, a known method, for example, pattern matching using a pattern of the lane marking type, or a lane marking model in which periodicity for each lane marking type is modeled may be used.” wherein a lane template is a lane marking model) (Sakai - [0060] “Lane markings within a certain range are associated by using a distance between approximate parameters calculated from a point sequence indicating a lane marking shape. As a parameter approximating the point sequence, respective approximation parameters of a straight line, a quadratic curve, and a circle are obtained, distances between the approximation parameters and the point sequence are calculated, and a parameter having the smallest distance is set as the most suitable approximation parameter.”); and
determining the lane template based on map information corresponding to the driving road.
The motivation for combining Sakai and Han is the same motivation as used for claim 1.
Regarding claim 5, Sakai in view of Han teaches the method of claim 3, wherein the determining the matching score comprises:
sweeping the lane template into pixels of the candidate lane lines by moving and rotating the lane template (Sakai - [0040] “For the estimation of the lane marking type, a known method, for example, pattern matching using a pattern of the lane marking type, or a lane marking model in which periodicity for each lane marking type is modeled may be used.” wherein a lane template is a lane marking model) (Sakai - [0059] “First, in S221, the lane marking type integration unit 220 converts coordinates of data acquired by the plurality of sensors 110 to integrate coordinate systems. For example, a coordinate system centered on the own vehicle is defined as +x for the front, +y for the left, +z for the vertically upward direction, and + for rotation counterclockwise around each axis with the center of tires of the rear part of the vehicle as the origin. Therefore, by converting each data such that this offset is eliminated, all the data can be converted on the same coordinate system.” wherein sweeping by moving and rotating the lane template is converting on the same coordinate system by centering and rotating); and
determining a matching score of the candidate lane lines based on a number of pixels of the candidate lane lines matching the lane template through the sweeping (Sakai - [0057] “As to which lane marking shape model the graph shape obtained by plotting the luminance detected for each sampling line matches, it is preferable to select the most matching one as a result of the template matching. Although a deviation occurs between the detected graph shape of the luminance and the lateral position of the lane marking shape model, this deviation is eliminated by shifting the graph shape by the vehicle lateral position of the lane marking. The final calculation for each lane marking type is determined on the basis of how much each lane shape graph has appeared in a determined region,” wherein a number of pixels matching through the sweeping is each lane shape graph that matches after shifting the graph shape).
The motivation for combining Sakai and Han is the same motivation as used for claim 1.
Regarding claim 6, Sakai in view of Han teaches the method of claim 1, wherein the detecting whether there is the change in the road structure comprises:
detecting whether there is the change in the road structure by using any one or any combination of any two or more of a number of candidate lane lines comprised in the driving road based on the result of the matching, spacing information between the candidate lane lines, and information on the surrounding object (Sakai - [0050] “First, in S201, the lane marking detection unit 200 extracts a line segment that is a lane marking candidate from acquired data. Lane marking candidates include not only general lane markings including a white line and an orange line but also lane markings including Botts' Dots, cat's eyes, and the like.” wherein a number of candidate lane lines is lane marking candidates) (Sakai - [0053] “Next, in S204, the lane marking detection unit 200 extracts a lane marking from the lane marking candidates. Here, the lane marking is a line having a constant width. FIG. 5 illustrates an example of a method of detecting a lane marking having a constant width.” wherein spacing information is constant width) (Sakai - [0040] “For the estimation of the lane marking type, a known method, for example, pattern matching using a pattern of the lane marking type, or a lane marking model in which periodicity for each lane marking type is modeled may be used. The lane marking type candidate estimation unit 210 outputs the lane marking type determination result to the lane marking type integration unit 220.”).
The motivation for combining Sakai and Han is the same motivation as used for claim 1.
Regarding claim 7, Sakai in view of Han teaches the method of claim 6, wherein the detecting whether there is the change in the road structure comprises:
determining a first number of lane lines comprised in the driving road from either one or both of navigation information of the vehicle and map information corresponding to the driving road (Sakai - [0099] “Thus, in a case where there is an intention to update the map, the lane marking type may be estimated by using the method of Example 1 with the lane marking type obtained from the map information 400 as an initial value. In a case where a map in which a lane marking type or the like is not registered, such as a navigation map,” wherein a first number of lane lines are the lane marking type obtained from a navigation map);
determining a second number of candidate lane lines of which a matching score is higher than a reference value among the candidate lane lines (Sakai - [0057] “Next, in S212, the lane marking type candidate estimation unit 210 collates a graph shape obtained by plotting the luminance of the lane marking of the road surface for each sampling interval extracted in S211 with the model having the lane marking shape obtained by modeling the luminance in advance by using a known method such as template matching to obtain the matching degree…As to which lane marking shape model the graph shape obtained by plotting the luminance detected for each sampling line matches, it is preferable to select the most matching one as a result of the template matching.” wherein a second number of candidate lines is the lane marking type candidates and a matching score is the matching degree; It is obvious to one of ordinary skill in the art that a matching score is higher than a reference value because Sakai teaches using a most matching degree); and
detecting whether there is the change in the road structure based on whether there is a difference between the first number of lane lines and the second number of candidate lane lines (Sakai - [0098] “Next, in S224′, the lane marking type candidate estimation unit 210 associates the map information 400 of which the coordinates have been converted in S223′ with the lane marking detected by the own vehicle.” wherein the first number of lane lines are of the map information and the second number of candidate lane lines are of the coordinates that have been converted) (Sakai - [0042] “In a case where a plurality of lane marking types are assigned to one integrated lane marking as a result of integration of lane markings in the lane marking type integration unit 220, the lane marking type branch point estimation unit 230 detects a point at which a lane marking type changes. A point at which a lane marking type changes may be detected by using a ratio of lane marking types included in the integrated lane marking. In a case where a change point at which the lane marking type changes is found, a region of the lane marking before and after the change point is divided into regions, and lane marking types are assigned to the regions after division.” wherein a change or a loss of the lane line is the change point at which the lane marking type changes are found).
The motivation for combining Sakai and Han is the same motivation as used for claim 1.
Regarding claim 8, Sakai in view of Han teaches the method of claim 6, wherein the detecting whether there is the change in the road structure comprises:
determining the spacing information between the candidate lane lines by using either one or both of map information corresponding to the driving road and width information of a driving lane in which the vehicle is driving (Sakai - [0050] “First, in S201, the lane marking detection unit 200 extracts a line segment that is a lane marking candidate from acquired data. Lane marking candidates include not only general lane markings including a white line and an orange line but also lane markings including Botts' Dots, cat's eyes, and the like.” wherein a number of candidate lane lines is lane marking candidates) (Sakai - [0053] “Next, in S204, the lane marking detection unit 200 extracts a lane marking from the lane marking candidates. Here, the lane marking is a line having a constant width. FIG. 5 illustrates an example of a method of detecting a lane marking having a constant width.” wherein spacing information is constant width) (Sakai - [0094] “The map information 400 may be either a high-precision map in which information such as a width of a road, a lane marking, connection between lanes, and the like is described in detail, or a navigation map including intersection information and the like. By using lane marking information registered in the map information 400 and information such as the number of lanes and a road width, it is possible to more accurately determine a lane marking type, estimate a lane marking branch point, and integrate lane marking information.”); and
detecting whether there is the change in the road structure based on a number of pairs of valid lane lines based on the spacing information (Sakai - [0094] “By using lane marking information registered in the map information 400 and information such as the number of lanes and a road width, it is possible to more accurately determine a lane marking type, estimate a lane marking branch point, and integrate lane marking information.” wherein a number of pairs of valid lane lines based on spacing information is the number of lanes and a road width).
The motivation for combining Sakai and Han is the same motivation as used for claim 1.
Regarding claim 9, Sakai in view of Han teaches the method of claim 6, wherein the detecting whether there is the change in the road structure comprises:
assigning a penalty value of the candidate lane lines by using the information on the surrounding object comprising a surrounding vehicle (Sakai - [0096] “Next, in S222′, the lane marking type candidate estimation unit 210 reads surrounding information of the own vehicle from the map information 400. The surrounding information is limited by the map information 400 to be used.” wherein information on the surrounding object is the surrounding information), wherein the penalty value corresponds to candidate lane lines among the candidate lane lines in an area in which the surrounding vehicle is positioned (Sakai - [0091] “In this case, a formula for likelihood integration using a reliability is calculated by substituting a weight as in the following Formulas (2) and (3) into P (Sfc|Ti) expressed in Formula (1). Here, 0 indicates a state in which the reliability is the lowest, and 1 indicates a state in which the reliability is the highest.” wherein a penalty value is 0); and
based on the penalty value (Sakai - [0091] “In this case, a formula for likelihood integration using a reliability is calculated by substituting a weight as in the following Formulas (2) and (3) into P (Sfc|Ti) expressed in Formula (1). Here, 0 indicates a state in which the reliability is the lowest, and 1 indicates a state in which the reliability is the highest.” wherein a penalty value is 0), detecting whether there is the change in the road structure comprising candidate lane lines corresponding to the area in which the surrounding vehicle is positioned (Sakai - [0042] “In a case where a plurality of lane marking types are assigned to one integrated lane marking as a result of integration of lane markings in the lane marking type integration unit 220, the lane marking type branch point estimation unit 230 detects a point at which a lane marking type changes.” wherein the change in the road structure is a point at which a lane marking type changes) (Sakai - [0050] “First, in S201, the lane marking detection unit 200 extracts a line segment that is a lane marking candidate from acquired data. Lane marking candidates include not only general lane markings including a white line and an orange line but also lane markings including Botts' Dots, cat's eyes, and the like. It is possible to detect a lane marking candidate from a point sequence acquired by the LiDAR by using a method of performing road surface estimation on the basis of a vehicle posture and extracting a point sequence having a reflection intensity equal to or higher than a certain level among point sequences on a road surface.” wherein a number of candidate lane lines is lane marking candidates).
The motivation for combining Sakai and Han is the same motivation as used for claim 1.
Regarding claim 10, Sakai in view of Han teaches the method of claim 9, wherein the assigning the penalty value comprises:
assigning a penalty value of candidate lane lines of a lane in which the surrounding vehicle is driving (Sakai - [0091] “In this case, a formula for likelihood integration using a reliability is calculated by substituting a weight as in the following Formulas (2) and (3) into P (Sfc|Ti) expressed in Formula (1). Here, 0 indicates a state in which the reliability is the lowest, and 1 indicates a state in which the reliability is the highest.” wherein a penalty value is 0) (Sakai - [0050] “First, in S201, the lane marking detection unit 200 extracts a line segment that is a lane marking candidate from acquired data. Lane marking candidates include not only general lane markings including a white line and an orange line but also lane markings including Botts' Dots, cat's eyes, and the like.” wherein a number of candidate lane lines is lane marking candidates), among the candidate lane lines, based on whether a distance between the surrounding vehicle and right and left lane lines among the candidate lane lines of the surrounding vehicle based on a position and width of the surrounding vehicle is within a threshold value (Sakai - [0046] “The detection of a lane marking using each functional block described above may be performed separately for lane markings on the left side and the right side of the vehicle.”) (Sakai - [0050] “First, in S201, the lane marking detection unit 200 extracts a line segment that is a lane marking candidate from acquired data. Lane marking candidates include not only general lane markings including a white line and an orange line but also lane markings including Botts' Dots, cat's eyes, and the like. It is possible to detect a lane marking candidate from a point sequence acquired by the LiDAR by using a method of performing road surface estimation on the basis of a vehicle posture and extracting a point sequence having a reflection intensity equal to or higher than a certain level among point sequences on a road surface.” wherein a number of candidate lane lines is lane marking candidates) (Sakai - [0096] “The surrounding information is limited by the map information 400 to be used. In the present example, on the assumption that a high-precision map is used, a lane marking type, a lane marking position, the number of lanes, a road width, and lane connection information are read. A range in which the map information 400 is read is information within a predetermined distance from the own vehicle.” wherein a threshold value is a predetermined distance).
The motivation for combining Sakai and Han is the same motivation as used for claim 1.
Regarding claim 11, Sakai in view of Han teaches the method of claim 1, wherein the determining the lane line information comprises:
determining the lane line information by using the road feature information and a matching score of candidate lane lines recognized through the matching in response to the change in the road structure not being detected (Sakai - [0040] “For the estimation of the lane marking type, a known method, for example, pattern matching using a pattern of the lane marking type, or a lane marking model in which periodicity for each lane marking type is modeled may be used. The lane marking type candidate estimation unit 210 outputs the lane marking type determination result to the lane marking type integration unit 220.” wherein lane line information is lane marking type) (Sakai - [0094] “The map information 400 is used for processing of the lane marking type candidate estimation unit 210, the lane marking type branch point estimation unit 230, and the lane marking information integration unit 260 in FIG. 24.” wherein the road feature information is the map information) (Sakai - [0057] “Next, in S212, the lane marking type candidate estimation unit 210 collates a graph shape obtained by plotting the luminance of the lane marking of the road surface for each sampling interval extracted in S211 with the model having the lane marking shape obtained by modeling the luminance in advance by using a known method such as template matching to obtain the matching degree.” wherein a matching score is the matching degree).
The motivation for combining Sakai and Han is the same motivation as used for claim 1.
Regarding claim 12, Sakai in view of Han teaches the method of claim 1, wherein the determining the lane line information comprises:
determining the lane line information by using a penalty value assigned (Sakai - [0091] “In this case, a formula for likelihood integration using a reliability is calculated by substituting a weight as in the following Formulas (2) and (3) into P (Sfc|Ti) expressed in Formula (1). Here, 0 indicates a state in which the reliability is the lowest, and 1 indicates a state in which the reliability is the highest.” wherein a penalty value is 0) by using the road feature information (Sakai - [0094] “The map information 400 is used for processing of the lane marking type candidate estimation unit 210, the lane marking type branch point estimation unit 230, and the lane marking information integration unit 260 in FIG. 24.” wherein the road feature information is the map information), candidate lane lines recognized through the matching (Sakai - [0050] “First, in S201, the lane marking detection unit 200 extracts a line segment that is a lane marking candidate from acquired data. Lane marking candidates include not only general lane markings including a white line and an orange line but also lane markings including Botts' Dots, cat's eyes, and the like.” wherein a number of candidate lane lines is lane marking candidates) (Sakai - [0057] “Next, in S212, the lane marking type candidate estimation unit 210 collates a graph shape obtained by plotting the luminance of the lane marking of the road surface for each sampling interval extracted in S211 with the model having the lane marking shape obtained by modeling the luminance in advance by using a known method such as template matching to obtain the matching degree.”), and the information on the surrounding object in response to the change in the road structure being detected (Sakai - [0096] “Next, in S222′, the lane marking type candidate estimation unit 210 reads surrounding information of the own vehicle from the map information 400. The surrounding information is limited by the map information 400 to be used.” wherein information on the surrounding object is the surrounding information).
The motivation for combining Sakai and Han is the same motivation as used for claim 1.
Regarding claim 13, Sakai in view of Han teaches the method of claim 12, wherein the determining the lane line information in response to the change in the road structure being detected comprises:
determining reliability of each of the candidate lane lines of the driving road based the penalty value (Sakai - [0044] “The lane marking reliability estimation unit 250 adds a reliability to the detected lane marking.”) (Sakai - [0050] “First, in S201, the lane marking detection unit 200 extracts a line segment that is a lane marking candidate from acquired data. Lane marking candidates include not only general lane markings including a white line and an orange line but also lane markings including Botts' Dots, cat's eyes, and the like.” wherein a number of candidate lane lines is lane marking candidates) (Sakai - [0091] “In this case, a formula for likelihood integration using a reliability is calculated by substituting a weight as in the following Formulas (2) and (3) into P (Sfc|Ti) expressed in Formula (1). Here, 0 indicates a state in which the reliability is the lowest, and 1 indicates a state in which the reliability is the highest.” wherein a penalty value is 0); and
determining, based on the reliability of each of the candidate lane lines (Sakai - [0044] “The lane marking reliability estimation unit 250 adds a reliability to the detected lane marking.”) (Sakai - [0050] “First, in S201, the lane marking detection unit 200 extracts a line segment that is a lane marking candidate from acquired data. Lane marking candidates include not only general lane markings including a white line and an orange line but also lane markings including Botts' Dots, cat's eyes, and the like.” wherein a number of candidate lane lines is lane marking candidates), the lane line information of the driving road by fitting the candidate lane lines to lane lines of the driving road (Sakai - [0040] “For the estimation of the lane marking type, a known method, for example, pattern matching using a pattern of the lane marking type, or a lane marking model in which periodicity for each lane marking type is modeled may be used. The lane marking type candidate estimation unit 210 outputs the lane marking type determination result to the lane marking type integration unit 220.” wherein lane line information is lane marking type).
The motivation for combining Sakai and Han is the same motivation as used for claim 1.
Regarding claim 14, Sakai in view of Han teaches the method of claim 13, wherein the determining the lane line information of the driving road by fitting the candidate lane lines to the lane lines of the driving road comprises:
determining a driving equation corresponding to the driving road based on the reliability of each candidate lane line (Sakai - [0057] “In the present example, a detection range is set to 50 m, a lane marking type is obtained in a range of every 10 m, and a likelihood of the corresponding lane marking type is added by 0.2 to obtain a likelihood of each lane marking type. In the present example, although a likelihood is calculated by using such a method, a likelihood of a lane marking type may be calculated by using Bayesian estimation or the like on the basis of lane marking information being tracked.” wherein a driving equation is the Bayesian estimation) (Sakai - [0044] “The lane marking reliability estimation unit 250 adds a reliability to the detected lane marking.”) (Sakai - [0050] “First, in S201, the lane marking detection unit 200 extracts a line segment that is a lane marking candidate from acquired data. Lane marking candidates include not only general lane markings including a white line and an orange line but also lane markings including Botts' Dots, cat's eyes, and the like.” wherein a number of candidate lane lines is lane marking candidates); and
determining the lane line information by tracking multiple lane lines of the driving road by each lane line by using the driving equation (Sakai - [0040] “For the estimation of the lane marking type, a known method, for example, pattern matching using a pattern of the lane marking type, or a lane marking model in which periodicity for each lane marking type is modeled may be used. The lane marking type candidate estimation unit 210 outputs the lane marking type determination result to the lane marking type integration unit 220.” wherein lane line information is lane marking type) (Sakai - [0057] “In the present example, a detection range is set to 50 m, a lane marking type is obtained in a range of every 10 m, and a likelihood of the corresponding lane marking type is added by 0.2 to obtain a likelihood of each lane marking type. In the present example, although a likelihood is calculated by using such a method, a likelihood of a lane marking type may be calculated by using Bayesian estimation or the like on the basis of lane marking information being tracked.” wherein a driving equation is the Bayesian estimation).
The motivation for combining Sakai and Han is the same motivation as used for claim 1.
Regarding claim 15, Sakai in view of Han teaches the method of claim 14, further comprising:
recognizing a position of the vehicle by using sensors comprised in the vehicle (Sakai - [0038] “Data acquired by a sensor 110 attached to the own vehicle and vehicle information 120 acquired from the own vehicle via a network such as CAN are input to the driving assistance device 100.”);
extracting map property information of the driving road based on a current position of the vehicle from map information by using positioning information determined through the position of the vehicle (Sakai - [0095] “The lane marking type candidate estimation unit 210 estimates lane marking type candidates in consideration of the map information 400. First, in S221′, the lane marking type candidate estimation unit 210 calculates which position on the map a position of the own vehicle corresponds to in order to read the map information 400.” wherein map property information is the map information) (Sakai - [0094] “The map information 400 is used for processing of the lane marking type candidate estimation unit 210, the lane marking type branch point estimation unit 230, and the lane marking information integration unit 260 in FIG. 24.” wherein the map information is the lane marking information);
detecting whether there is the change in the road structure in the map information by using the map property information (Sakai - [0042] “In a case where a plurality of lane marking types are assigned to one integrated lane marking as a result of integration of lane markings in the lane marking type integration unit 220, the lane marking type branch point estimation unit 230 detects a point at which a lane marking type changes.” wherein the change in the road structure is a point at which a lane marking type changes) (Sakai - [0095] “The lane marking type candidate estimation unit 210 estimates lane marking type candidates in consideration of the map information 400. First, in S221′, the lane marking type candidate estimation unit 210 calculates which position on the map a position of the own vehicle corresponds to in order to read the map information 400.” wherein map property information is the map information); and
modifying the map information by reflecting the change in the road structure when the change in the road structure is detected (Sakai - [0094] “The map information 400 is used for processing of the lane marking type candidate estimation unit 210, the lane marking type branch point estimation unit 230, and the lane marking information integration unit 260 in FIG. 24.” wherein the map information is the lane marking information) (Sakai - [0099] “Thus, in a case where there is an intention to update the map, the lane marking type may be estimated by using the method of Example 1 with the lane marking type obtained from the map information 400 as an initial value.”).
The motivation for combining Sakai and Han is the same motivation as used for claim 1.
Regarding claim 16, Sakai in view of Han teaches the method of claim 14, further comprising:
recognizing the position of the vehicle by using sensors comprised in the vehicle and the lane line information tracked by each lane line (Sakai - [0038] “Data acquired by a sensor 110 attached to the own vehicle and vehicle information 120 acquired from the own vehicle via a network such as CAN are input to the driving assistance device 100.”) (Sakai - [0105] “Therefore, lane markings to be tracked can be continuously recognized and thus the lane markings can be tracked with high accuracy.”);
extracting map property information of the driving road based on a current position of the vehicle by using the positioning information determined through the position of the vehicle (Sakai - [0095] “The lane marking type candidate estimation unit 210 estimates lane marking type candidates in consideration of the map information 400. First, in S221′, the lane marking type candidate estimation unit 210 calculates which position on the map a position of the own vehicle corresponds to in order to read the map information 400.” wherein map property information is the map information) (Sakai - [0094] “The map information 400 is used for processing of the lane marking type candidate estimation unit 210, the lane marking type branch point estimation unit 230, and the lane marking information integration unit 260 in FIG. 24.” wherein the map information is the lane marking information); and
detecting whether there is the change in the road structure by further using the map property information (Sakai - [0042] “In a case where a plurality of lane marking types are assigned to one integrated lane marking as a result of integration of lane markings in the lane marking type integration unit 220, the lane marking type branch point estimation unit 230 detects a point at which a lane marking type changes.” wherein the change in the road structure is a point at which a lane marking type changes) (Sakai - [0095] “The lane marking type candidate estimation unit 210 estimates lane marking type candidates in consideration of the map information 400. First, in S221′, the lane marking type candidate estimation unit 210 calculates which position on the map a position of the own vehicle corresponds to in order to read the map information 400.” wherein map property information is the map information).
The motivation for combining Sakai and Han is the same motivation as used for claim 1.
Regarding claim 17, the claim recites similar limitations to claim 1 but in the form of a non-transitory computer-readable storage medium (Sakai - [0037] “The driving assistance device 100 includes, for example, a CPU and a GPU which are arithmetic devices, and a RAM and a ROM which are storage devices, and can realize these functions by loading a predetermined program stored in the ROM onto the RAM and executing the program in the CPU.”). Therefore, claim 17 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Regarding claim 18, the claim recites similar limitations to claim 1 but in the form of an apparatus (Sakai - [0037] “The driving assistance device 100 includes, for example, a CPU and a GPU which are arithmetic devices, and a RAM and a ROM which are storage devices, and can realize these functions by loading a predetermined program stored in the ROM onto the RAM and executing the program in the CPU.”). Therefore, claim 18 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Regarding claim 19, the claim recites similar limitations to claim 6 but in the form of an apparatus. Therefore, claim 19 recites similar limitations to claim 6 and is rejected for similar rationale and reasoning (see the analysis for claim 6 above).
Regarding claim 20, the claim recites similar limitations to claims 12 and 13 but in the form of an apparatus. Therefore, claim 20 recites similar limitations to claims 12 and 13 and is rejected for similar rationale and reasoning (see the analysis for claims 12 and 13 above).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/AMANDA H PEARSON/Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666
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