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 .
Response to Arguments
Applicant's arguments filed 02/02/2026 have been fully considered.
Applicant argues Sengupta (US 11560131) does not disclose at least the features of expected heading angle or expected lateral error at a target point as recited in claim 1. Instead, Applicant argues Sengupta merely discloses calculating a radius of curvature based on the last n points and predicting lane marker using lane tangent. Applicant argues Nakamura (US 20200307612) fails to cure these deficiencies. Therefore, Applicant concludes independent claim 1 is allowable.
Indeed, Sengupta and Nakamura are found not to teach these features and therefore new reference Jiang (US 20210291862) has been necessitated, which teaches, in brief, predicting the heading of a vehicle over points in the future, which is then used to control the vehicle. As such these arguments are moot.
Applicant argues claims 8 and 16 recite similar features to claim 1 and therefore are allowable for the same reasons.
These arguments are moot for similar reasons as given above.
Applicant argues the dependent claims are allowable by virtue of their dependency.
This argument is unpersuasive as each independent and dependent claim has been fully rejected and for the reasons as given above.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3-9, 11-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sengupta et al. (US 11560131), in view of Jiang, in further view of Jiang et al. (US 20210291862).
In regards to claim 1, Sengupta teaches a device comprising: (Figs 1-7.)
a sensor; (Col 23 lines 33-50, camera sensors of vehicle observe environment and lane markings.)
memory storing instructions; (Col 10 lines 8-11, memory stores instructions for processing data.) and
a controller operatively connected to the sensor and the memory, (Col 10 lines 8-23, Col 24 lines 10-18, control system performs operations based on sensor information and instructions.)
wherein the instructions, when executed by the controller, cause the device to: (Col 10 lines 8-23, Col 24 lines 10-18, control system performs operations based on sensor information and instructions.)
obtain, via the sensor, a line recognition result associated with a road on which a vehicle is traveling; (Col 24 lines 8-37, image data is retrieved from the cameras and other sensors depicting lane markers and analyzed to determine if there are enough lane markers within the captured images to produce an adequate look-ahead horizon.)
determine, based on the line recognition result, whether information, which comprises at least one of a curvature of the road or a curvature change rate of the road, satisfies a specified condition; (Col 24 lines 8-57, image data is retrieved from the cameras and other sensors depicting lane markers and analyzed to determine if there are enough lane markers within the captured images to produce an adequate look-ahead horizon. The last n points are used to compute a radius of curvature of longest present lane marker, curvature is calculated and propagated to predict lane markers ahead by calculating a moving average from the curvature and curvature tangent. Curvature tangent is the rate of change of curvature. Col 25 lines 15-53, outlier points are detected and smoothed if the outlier is the first outlier in a row or taken into further account with curvature and curvature tangent if more than a threshold number of outliers are found in a row. The lack of sufficient lane markers to determine curvature and curvature change rate serves as a specified condition. The presence of a single outlier or multiple outliers in a row also serve a specified condition.)
based on the information satisfying the specified condition, generate calibrated line information using an expected curvature change rate; (Col 24 lines 8-57, Col 25 lines 15-53, when there are not enough lane markers within the captured images to provide an adequate look-ahead horizon, the last n points of the longest lane marker are used to compute a radius of curvature, which is then used to predict the lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature, while accounting for outliers and providing appropriate smoothing. This generates a calibrated lane line using an expected curvature and rate of change of curvature.) and
control movement of the vehicle based on the calibrated line information, wherein the expected curvature change rate is determined based on: (Col 24 lines 8-57, Col 25 lines 15-53, when there are not enough lane markers within the captured images to provide an adequate look-ahead horizon, the last n points of the longest lane marker are used to compute a radius of curvature, which is then used to predict the lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature, while accounting for outliers and providing appropriate smoothing. This generates a calibrated lane line using an expected curvature and rate of change of curvature. Col 23 lines 51-67, Col 24 lines 1-6, Col 29 lines 1-22, vehicle is controlled based on predictive lane markers ahead of the vehicle.)
the curvature change rate, (Col 24 lines 8-57, Col 25 lines 15-53, when there are not enough lane markers within the captured images to provide an adequate look-ahead horizon, the last n points of the longest lane marker are used to compute a radius of curvature, which is then used to predict the lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature, while accounting for outliers and providing appropriate smoothing. This generates a calibrated lane line using the determined curvature and rate of change of curvature.)
an expected curvature at a target point on the road, (Col 24 lines 8-57, Col 25 lines 15-53, when there are not enough lane markers within the captured images to provide an adequate look-ahead horizon, the last n points of the longest lane marker are used to compute a radius of curvature, which is then used to predict the lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature, while accounting for outliers and providing appropriate smoothing. This generates a calibrated lane line using an expected curvature and rate of change of curvature, which occurs over the n-second look-ahead requirement of the vehicle, which includes at least an expected curvature and rate of change of curvature at start and end points of the predictions, as well as every intermediate point.) and
Sengupta does not teach:
at least one of an expected heading angle of the vehicle at the target point on the road, or an expected lateral error at the target point.
However, Jiang teaches predicting a path of a vehicle over points including predicting heading ([0016]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control device of Sengupta, by incorporating the teachings of Jiang, such that an expected heading of the vehicle is determined and used to further control movement of the vehicle.
The motivation to do so is that, as acknowledged by Jiang, this allows for safer autonomous or semi-autonomous driving ([0005]).
In regards to claim 3, Sengupta, as modified by Jiang, teaches the device of claim 1, wherein the movement of the vehicle, based on the calibrated line information, is controlled, during a time duration after a time associated with the information. (Col 29 lines 1-22, vehicle controls steering according to the predicted lane markers at least for the look-ahead horizon, which is a time duration of n-seconds.)
In regards to claim 4, Sengupta, as modified by Jiang, teaches the device of claim 1, wherein the instructions, when executed by the controller, further cause the device to:
determine at least one target point candidate that the vehicle is expected to reach within a time duration after a time associated with the information; (Col 24 lines 8-57, Col 25 lines 15-53, when there are not enough lane markers within the captured images to provide an adequate look-ahead horizon, the last n points of the longest lane marker are used to compute a radius of curvature, which is then used to predict the lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature, while accounting for outliers and providing appropriate smoothing. This generates a calibrated lane line using an expected curvature and rate of change of curvature, which occurs over the n-second look-ahead requirement of the vehicle, which includes at least an expected curvature and rate of change of curvature at start and end points of the predictions, as well as every intermediate point, where the vehicle is expected to reach the start point, end point, and the intermediate points.) and
determine the expected curvature change rate at the target point, wherein the target point is one of the at least one target point candidate. (Col 24 lines 8-57, Col 25 lines 15-53, when there are not enough lane markers within the captured images to provide an adequate look-ahead horizon, the last n points of the longest lane marker are used to compute a radius of curvature, which is then used to predict the lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature, while accounting for outliers and providing appropriate smoothing. This generates a calibrated lane line using an expected curvature and rate of change of curvature, which occurs over the n-second look-ahead requirement of the vehicle, which includes at least an expected curvature and rate of change of curvature at start and end points of the predictions, as well as every intermediate point.)
In regards to claim 5, Sengupta, as modified by Jiang, teaches the device of claim 4, wherein the instructions, when executed by the controller, further cause the device to:
determine, among curvatures included in the line recognition result, a reference curvature associated with a second time before the time associated with the information; (Col 24 lines 8-57, when lane markers are recognized, if there are sufficient lane markers to acquire the required look-ahead horizon, then the vehicle is navigated based on the analyzed lane markers up to the look-ahead horizon, which includes using the corresponding curvature to navigate the vehicle. Additionally, curvature of the longest lane marker is determined when there are insufficient lane makers to reach the look-ahead horizon, which includes lane marker information and curvature acquired before the time with lane marker issue information.) and
determine, among the at least one target point candidate, the target point such that a difference between a calibrated curvature at a time point when the specified condition is satisfied and the reference curvature is smallest. (Col 24 lines 8-57, Col 25 lines 15-53, Col 26 lines 33-42, when there are not enough lane markers within the captured images to provide an adequate look-ahead horizon, the last n points of the longest lane marker are used to compute a radius of curvature, which is then used to predict the lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature, while accounting for outliers and providing appropriate smoothing. This generates a calibrated lane line using an expected curvature and rate of change of curvature, which occurs over the n-second look-ahead requirement of the vehicle, which includes at least an expected curvature and rate of change of curvature at start and end points of the predictions, as well as every intermediate point, where the vehicle is expected to reach the start point, end point, and the intermediate points. When the vehicle travels on straight sections of road, the rate of change of curvature and curvature are smallest, which is found as a particular point within the look ahead horizon.)
In regards to claim 6, Sengupta, as modified by Jiang, teaches the device of claim 1, wherein the instructions, when executed by the controller, further cause the device 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; (Col 24 lines 8-57, Col 25 lines 15-53, curvature tangents of the last n points of the longest lane marker are determined and propagated to predict the lane markers ahead. The curvature tangents are curvature change rates included in the line recognition result associated with a time associated with the information.)
determine, based on the curvature change rates included in the line recognition result, a second curvature change rate representing an overall curvature change rate of the road; (Col 24 lines 8-57, Col 25 lines 15-53, a moving average of the curvature tangent is determined which is an overall 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 starting at the time associated with the information, (Col 24 lines 8-57, Col 25 lines 15-53, curvature tangents of the last n points of the longest lane marker are determined and propagated to predict the lane markers ahead, including the curvature and curvature tangents of the lane markers ahead. Fitting is performed to fit the lane markers by using weights inversely proportional to the variance of the associated with the n points.)
wherein the weight of each of the first curvature change rate and the second curvature change rate comprises a first weight of the first curvature change rate, wherein the first weight decreases during the time duration. (Col 24 lines 8-57, Col 25 lines 15-53, fitting is performed over the last n points during a moving average which averages the last set of a number of points, weighting is performed on the last n points, which while the moving average moves, adjusts the weighting applied with the moving average as fewer points are selected and variance is reduced through smoothing, the propagated lane markers use reducing weights because variance is reduced.)
In regards to claim 7, Sengupta, as modified by Jiang, teaches the device of claim 6, wherein the instructions, when executed by the controller, further cause the device to:
generate new line information based on the expected curvature change rate; (Col 24 lines 8-57, Col 25 lines 15-53, when there are not enough lane markers within the captured images to provide an adequate look-ahead horizon, the last n points of the longest lane marker are used to compute a radius of curvature, which is then used to predict the lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature, while accounting for outliers and providing appropriate smoothing. This generates a new lane line using an expected curvature and rate of change of curvature.) and
generate the calibrated line information by replacing information on at least a portion of a line section included in the line recognition result with the new line information. (Col 24 lines 8-57, Col 25 lines 15-53, when there are not enough lane markers within the captured images to provide an adequate look-ahead horizon, the last n points of the longest lane marker are used to compute a radius of curvature, which is then used to predict the lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature, while accounting for outliers and providing appropriate smoothing. This generates a new lane line using an expected curvature and rate of change of curvature by replacing information after the last n points with new information included in the line recognition result with the new line information.)
In regards to claim 8, Sengupta, as modified by Jiang, teaches the device of claim 1, wherein the instructions, when executed by the controller, further cause the device to:
determine, via the sensor, a travel speed of the vehicle; (Col 7 lines 28-56, Col 10 lines 27-30, odometry sensors may operate to determine wheel speed, which indicates vehicle speed, and control the vehicle.) and
determine the target point based on at least one of the travel speed or a predetermined time duration. (Col 24 lines 8-37, an n-second look ahead requirement may be determined for safe driving within which the lane markers are propagated.)
In regards to claim 9, Sengupta teaches a method comprising: (Figs 8-10.)
obtaining, by a controller and via a sensor, a line recognition result associated with a road on which a vehicle is traveling; (Col 24 lines 8-37, image data is retrieved from the cameras and other sensors depicting lane markers and analyzed to determine if there are enough lane markers within the captured images to produce an adequate look-ahead horizon.)
determining, by the controller and based on the line recognition result, whether information, which comprises at least one of a curvature of the road or a curvature change rate of the road, satisfies a specified condition; (Col 24 lines 8-57, image data is retrieved from the cameras and other sensors depicting lane markers and analyzed to determine if there are enough lane markers within the captured images to produce an adequate look-ahead horizon. The last n points are used to compute a radius of curvature of longest present lane marker, curvature is calculated and propagated to predict lane markers ahead by calculating a moving average from the curvature and curvature tangent. Curvature tangent is the rate of change of curvature. Col 25 lines 15-53, outlier points are detected and smoothed if the outlier is the first outlier in a row or taken into further account with curvature and curvature tangent if more than a threshold number of outliers are found in a row. The lack of sufficient lane markers to determine curvature and curvature change rate serves as a specified condition. The presence of a single outlier or multiple outliers in a row also serve a specified condition.)
based on the information satisfying the specified condition, generating, by the controller, calibrated line information using an expected curvature change rate; (Col 24 lines 8-57, Col 25 lines 15-53, when there are not enough lane markers within the captured images to provide an adequate look-ahead horizon, the last n points of the longest lane marker are used to compute a radius of curvature, which is then used to predict the lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature, while accounting for outliers and providing appropriate smoothing. This generates a calibrated lane line using an expected curvature and rate of change of curvature.) and
controlling, by the controller, movement of the vehicle based on the calibrated line information, wherein the expected curvature change rate is determined based on: (Col 24 lines 8-57, Col 25 lines 15-53, when there are not enough lane markers within the captured images to provide an adequate look-ahead horizon, the last n points of the longest lane marker are used to compute a radius of curvature, which is then used to predict the lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature, while accounting for outliers and providing appropriate smoothing. This generates a calibrated lane line using an expected curvature and rate of change of curvature. Col 23 lines 51-67, Col 24 lines 1-6, Col 29 lines 1-22, vehicle is controlled based on predictive lane markers ahead of the vehicle.)
the curvature change rate, (Col 24 lines 8-57, Col 25 lines 15-53, when there are not enough lane markers within the captured images to provide an adequate look-ahead horizon, the last n points of the longest lane marker are used to compute a radius of curvature, which is then used to predict the lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature, while accounting for outliers and providing appropriate smoothing. This generates a calibrated lane line using the determined curvature and rate of change of curvature.)
an expected curvature at a target point on the road, (Col 24 lines 8-57, Col 25 lines 15-53, when there are not enough lane markers within the captured images to provide an adequate look-ahead horizon, the last n points of the longest lane marker are used to compute a radius of curvature, which is then used to predict the lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature, while accounting for outliers and providing appropriate smoothing. This generates a calibrated lane line using an expected curvature and rate of change of curvature, which occurs over the n-second look-ahead requirement of the vehicle, which includes at least an expected curvature and rate of change of curvature at start and end points of the predictions, as well as every intermediate point.) and
Sengupta does not teach:
at least one of an expected heading angle of the vehicle at the target point on the road, or an expected lateral error at the target point.
However, Jiang teaches predicting a path of a vehicle over points including predicting heading ([0016]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Sengupta, by incorporating the teachings of Jiang, such that an expected heading of the vehicle is determined and used to further control movement of the vehicle.
The motivation to do so is that, as acknowledged by Jiang, this allows for safer autonomous or semi-autonomous driving ([0005]).
In regards to claim 11, Sengupta, as modified by Jiang, teaches the method of claim 9.
Claim 11 recites a method having substantially the same features of claim 3 above, therefore claim 11 is rejected for the same reasons as claim 3.
In regards to claim 12, Sengupta, as modified by Jiang, teaches the method of claim 9.
Claim 12 recites a method having substantially the same features of claim 4 above, therefore claim 12 is rejected for the same reasons as claim 4.
In regards to claim 13, Sengupta, as modified by Jiang, teaches the method of claim 12.
Claim 13 recites a method having substantially the same features of claim 5 above, therefore claim 13 is rejected for the same reasons as claim 5.
In regards to claim 14, Sengupta, as modified by Jiang, teaches the method of claim 9.
Claim 14 recites a method having substantially the same features of claim 6 above, therefore claim 14 is rejected for the same reasons as claim 6.
In regards to claim 15, Sengupta, as modified by Jiang, teaches the method of claim 14.
Claim 15 recites a method having substantially the same features of claim 7 above, therefore claim 15 is rejected for the same reasons as claim 7.
In regards to claim 16, Sengupta teaches a non-transitory computer-readable medium storing instructions that, when executed, cause: (Col 10 lines 8-23, Col 24 lines 10-18, control system performs operations based on sensor information and instructions stored in memory.)
obtaining, by a controller and via a sensor, a line recognition result associated with a road on which a vehicle is traveling; (Col 24 lines 8-37, image data is retrieved from the cameras and other sensors depicting lane markers and analyzed to determine if there are enough lane markers within the captured images to produce an adequate look-ahead horizon.)
determining, by the controller and based on the line recognition result, whether information, which comprises at least one of a curvature of the road or a curvature change rate of the road, satisfies a specified condition; (Col 24 lines 8-57, image data is retrieved from the cameras and other sensors depicting lane markers and analyzed to determine if there are enough lane markers within the captured images to produce an adequate look-ahead horizon. The last n points are used to compute a radius of curvature of longest present lane marker, curvature is calculated and propagated to predict lane markers ahead by calculating a moving average from the curvature and curvature tangent. Curvature tangent is the rate of change of curvature. Col 25 lines 15-53, outlier points are detected and smoothed if the outlier is the first outlier in a row or taken into further account with curvature and curvature tangent if more than a threshold number of outliers are found in a row. The lack of sufficient lane markers to determine curvature and curvature change rate serves as a specified condition. The presence of a single outlier or multiple outliers in a row also serve a specified condition.)
based on the information satisfying the specified condition, generating, by the controller, calibrated line information using an expected curvature change rate; (Col 24 lines 8-57, Col 25 lines 15-53, when there are not enough lane markers within the captured images to provide an adequate look-ahead horizon, the last n points of the longest lane marker are used to compute a radius of curvature, which is then used to predict the lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature, while accounting for outliers and providing appropriate smoothing. This generates a calibrated lane line using an expected curvature and rate of change of curvature.) and
controlling movement of the vehicle based on the calibrated line information, wherein the expected curvature change rate is determined based on: (Col 24 lines 8-57, Col 25 lines 15-53, when there are not enough lane markers within the captured images to provide an adequate look-ahead horizon, the last n points of the longest lane marker are used to compute a radius of curvature, which is then used to predict the lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature, while accounting for outliers and providing appropriate smoothing. This generates a calibrated lane line using an expected curvature and rate of change of curvature. Col 23 lines 51-67, Col 24 lines 1-6, Col 29 lines 1-22, vehicle is controlled based on predictive lane markers ahead of the vehicle.)
the curvature change rate, (Col 24 lines 8-57, Col 25 lines 15-53, when there are not enough lane markers within the captured images to provide an adequate look-ahead horizon, the last n points of the longest lane marker are used to compute a radius of curvature, which is then used to predict the lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature, while accounting for outliers and providing appropriate smoothing. This generates a calibrated lane line using the determined curvature and rate of change of curvature.)
an expected curvature at a target point on the road, (Col 24 lines 8-57, Col 25 lines 15-53, when there are not enough lane markers within the captured images to provide an adequate look-ahead horizon, the last n points of the longest lane marker are used to compute a radius of curvature, which is then used to predict the lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature, while accounting for outliers and providing appropriate smoothing. This generates a calibrated lane line using an expected curvature and rate of change of curvature, which occurs over the n-second look-ahead requirement of the vehicle, which includes at least an expected curvature and rate of change of curvature at start and end points of the predictions, as well as every intermediate point.) and
Sengupta does not teach:
at least one of an expected heading angle of the vehicle at the target point on the road, or an expected lateral error at the target point.
However, Jiang teaches predicting a path of a vehicle over points including predicting heading ([0016]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control instructions of Sengupta, by incorporating the teachings of Jiang, such that an expected heading of the vehicle is determined and used to further control movement of the vehicle.
The motivation to do so is that, as acknowledged by Jiang, this allows for safer autonomous or semi-autonomous driving ([0005]).
In regards to claim 18, Sengupta, as modified by Jiang, teaches the non-transitory computer-readable medium of claim 16.
Claim 18 recites a medium having substantially the same features of claim 3 above, therefore claim 18 is rejected for the same reasons as claim 3.
In regards to claim 19, Sengupta, as modified by Jiang, teaches the non-transitory computer-readable medium of claim 16.
Claim 19 recites a medium having substantially the same features of claim 4 above, therefore claim 19 is rejected for the same reasons as claim 4.
In regards to claim 20, Sengupta, as modified by Jiang, teaches the non-transitory computer-readable medium of claim 19.
Claim 20 recites a medium having substantially the same features of claim 5 above, therefore claim 20 is rejected for the same reasons as claim 5.
Claims 2, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Sengupta, in view of Jiang, in further view of Nakamura et al. (US 20200307612).
In regards to claim 2, Sengupta, as modified by Jiang, teaches the device of claim 1, wherein the specified condition comprises:
a first condition comprising one of: (Col 24 lines 8-67, Col 25 lines 1-53, angle between curvature tangents over time is determined and smoothed using a weighted average and compared against a threshold to determine outliers, which includes cases of the first tangent being smaller than or larger than the second tangent, while curvature is continued to be determined, and by definition of having a changing tangent curvature is either increasing or decreasing.)
an increase in the curvature change rate and a decrease in the curvature, (Col 24 lines 8-67, Col 25 lines 1-53, angle between curvature tangents over time is determined and smoothed using a weighted average and compared against a threshold to determine outliers, which includes cases of the first tangent being smaller than or larger than the second tangent, while curvature is continued to be determined, and by definition of having a changing tangent curvature is either increasing or decreasing. This particularly includes at least an increase in the curvature change rate and a decrease in the curvature.) or
a decrease in the curvature change rate and an increase in the curvature, (Col 24 lines 8-67, Col 25 lines 1-53, angle between curvature tangents over time is determined and smoothed using a weighted average and compared against a threshold to determine outliers, which includes cases of the first tangent being smaller than or larger than the second tangent, while curvature is continued to be determined, and by definition of having a changing tangent curvature is either increasing or decreasing. This particularly includes at least a decrease in the curvature change range and an instead in the curvature.) and
Sengupta, as modified by Jiang, does not teach:
a second condition comprising the curvature changing by more than a threshold value.
However, Nakamura teaches performing a check on the difference between a first and second curvature against a threshold value, which prompts different control settings based on result of the comparison ([0038]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control device of Sengupta, by incorporating the teachings of Nakamura, such that a check on the curvature change against a threshold is determined and used along with the criteria for generating lane markings as in Sengupta.
The motivation to do so is that, as acknowledged by Nakamura, this allows for improved control and improved comfort ([0004]-[0006]).
In regards to claim 10, Sengupta, as modified by Jiang, teaches the method of claim 9.
Claim 10 recites a method having substantially the same features of claim 2 above, therefore claim 10 is rejected for the same reasons as claim 2.
In regards to claim 17, Sengupta, as modified by Jiang, teaches the non-transitory computer-readable medium of claim 16.
Claim 17 recites a medium having substantially the same features of claim 2 above, therefore claim 17 is rejected for the same reasons as claim 2.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ryu et al. (US 20160159394) teaches determining that lane lines have been misrecognized and correcting the lane line misrecognition for vehicle control.
Sato et al. (US 20240294185) teaches determining changing curvature for vehicle paths to improve navigation of curving roads.
Kobayashi (US 8346427) teaches determining expected curvature and rate of change of curvature of lane markers.
Matsunaga (US 20180374352) teaches determining curvature and curvature rate of change over time of a lane centerline.
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 mailing date of this final action.
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