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 06/25/2026 have been fully considered, but they are not persuasive.
In regards to independent claim 1, Applicant argues the cited combination of references fails to disclose or suggest at least the newly amended features of the claim. Applicant argues the Office has admitted Sengupta (US 11560131) does not teach at least expected heading angle feature and Jiang (US 20210291862) does not cure this deficiency. Applicant argues the characterization of Jiang pointed to does not meet the claimed limitation. Applicant argues, in Jiang, heading is a predicted output of the system, which is a result of path tracking, but is not used to determine any curvature related parameter, and instead is only used as a cost function for optimizing control commands. Therefore, Applicant concludes, even if Jiang were added to Sengupta, the combination would still lack the required teaching and the claim is allowable.
However, Sengupta teaches using the last n points of the longest lane marker to compute a radius of curvature to predict lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature. This generates a calibrated lane line as the vehicle travels based on the imaged lane features, where the last n number of points of lane line that are imaged are updated as the vehicle travels and new points are imaged. Jiang then teaches at least, just as the Applicant readily admits, heading as a predicted output of the system at various points as a result of path tracking that is used as a cost function for optimizing control commands, such that, by the combination of these references, Sengupta modified by Jiang, the vehicle is controlled based at least in part upon the predicted heading which is an expected heading, and then, as the vehicle travels, the last n number of points are updated as new points are discovered, and the expected curvature is updated. This bases the expected curvature change rate at least upon the curvature change rate, the expected curvature at a target point, and expected heading at the target point, which is precisely what is required by the claim.
As such, this argument is unpersuasive.
In regards to independent claim 16, Applicant argues Jiang does not disclose an expected curvature change rate at all and instead deals with steering rate of change and acceleration rate of change, which do not constitute an expected rate of change. Applicant argues even if Jiang’s predicted heading could be construed as an expected heading angle, Jiang does not teach the use of the expected heading angle to determine the expected curvature change rate. Therefore, Applicant concludes that even if Jiang were added to Sengupta, the combination would still be deficient. Applicant continues, the cited combination fails to teach the recited expected lateral error used to determine the expected curvature change rate as claimed, and Jiang’s recited cross track error is used as a cost function to optimize control commands, not to determine curvature. Similarly, Applicant argues Sengupta does not teach such features. Therefore, Applicant concludes, the cited combination fails to teach the claim and the claim is allowable.
However, for at least similar reasons to above, this argument is unpersuasive. Jiang teaches predicting the heading based upon a path stretching ahead of the vehicle over points, which is then used to issue control commands. This exactly determines a heading angle that is expected at a target position. As explained above, when combined into Sengupta, this causes the vehicle to control travel based upon expected heading angle, which then causes the vehicle to generate new points along the longest lane marker, thereby updating the last n points, which are then used to generate an expected curvature change rate based at least in part upon the heading. This is not an argument of Jiang alone teaching this feature, as the Applicant appears to argue, but instead that one of ordinary skill in the art upon viewing Sengupta in view of Jiang would have arrived at the claim as a whole upon viewing the references in combination. Further, the recited lateral error features are only recited in the alternative and therefore are not required to fully reject the claim, but nonetheless, by the Applicant’s own explanation of Jiang, the combination of Sengupta in view of Jiang appears to teach these features as well, as the Applicant readily admits that Jiang teaches determining lateral error for use in control, and as this control is used to generate the last n points on lane markers, at least also uses the lateral error to generate the expected curvature change rate in the same way heading is used.
As such, this argument is unpersuasive.
Applicant argues independent claim 9 is allowable for the same reasons as independent claims 1 and 16.
This argument is unpersuasive for the same reasons as given above.
In regards to dependent claims 4, 12, and 19, Applicant argues Sengupta does not teach the newly amended features, as Sengupta’s curvature tangent is different from the recited expected curvature change rate, and Sengupta does not teach or suggest a curvature change rate at a first point included in the line recognition result as a basis for determining expected curvature change rate at the target point. Therefore, Applicant concludes dependent claims 4, 12, and 19 are allowable.
However, Sengupta teaches the last n number of points of the longest lane marker are used to compute a radius of curvature which is then used to predict lane markers up to a look-ahead horizon by propagating the curvature and tangent of curvature. A tangent of curvature is, by its very definition, the rate at which curvature changes. When it is propagated to an expected position, it becomes an expected tangent of curvature which is, by definition, an expected curvature change rate. This takes a tangent of curvature at a point of the n number of points, and directly uses it to determine a future expected change rate of curvature, which is exactly what is required by the claim. As the Applicant appears to provide no evidence for their assertion that a computed curvature tangent at an expected lane position is not an expected curvature change rate, it is difficult to fully respond. If the Applicant truly believes these arguments to be fundamental to their claims, the Examiner strongly encourages the Applicant to actually explain the details of their arguments, rather than merely providing broad conclusory statements that appear to be factually incorrect on their face.
As such, this argument is unpersuasive.
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.
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 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 using: (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, including expected curvature change rate, 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, including expected curvature change rate, 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, including expected curvature change rate, 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 while propagating lane curvature, including expected curvature change rate, which incorporates the predicted heading as the vehicle moves and the lane lines are updated with new information.
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 based on a curvature change rate at a first point included in the line recognition result, 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, and is based on the curvature change rate at a first point included in the line recognition result.)
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 is one at a first time point at which the specified condition is satisfied and decreases to zero at a second time point after the first time point 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, which at least at some point fully weights the point and at some point when the lane has propagated a number n+1 points past the relevant point, decreases weight of the point to zero, 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 using: (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, including expected curvature change rate, 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, including expected curvature change rate, 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, including expected curvature change rate, 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 while propagating lane curvature, including expected curvature change rate, which incorporates the predicted heading as the vehicle moves and the lane lines are updated with new information.
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 using: (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, including expected curvature change rate, 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, including expected curvature change rate, 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, including expected curvature change rate, 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, wherein the at least one of the expected heading angle of the vehicle or the expected lateral error is used to determine the expected curvature change rate.
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 while propagating lane curvature, including expected curvature change rate, which incorporates the predicted heading as the vehicle moves and the lane lines are updated with new information.
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, 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 increase in the curvature.) and
wherein the first condition is satisfied when a product of a curvature change rate difference and a curvature difference is negative. (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. Necessarily, when the tangent difference is increasing or decreasing and the curvature difference is traveling in the opposite direction, a product of these is negative because they are of opposite signs and will always multiply to a negative number, which is satisfied when generating lane line data.)
Sengupta, as modified by Jiang, does not teach:
a second condition comprising the curvature changing by more than a threshold values
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 medium of Sengupta, as already modified by Jiang, 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 the same as acknowledged by Nakamura in regards to 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.
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/MATTHIAS S WEISFELD/Examiner, Art Unit 3661
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