DETAILED ACTION
Claims 1-4, 7-13, and 16-24 are pending in this application and are being given the priority date of 6/16/2020 in accordance with applicant’s claim for foreign priority. Claims 1 10 and 19 are amended, claims 5-6 and 14-15 are canceled, and claims 20-24 are newly added.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN 112753038, filed on 6/16/2020.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/08/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/05/2025 has been entered.
Response to Arguments
35 U.S.C. 103
Applicant’s arguments (see Remarks filed 12/05/2025) regarding the rejections made under 35 U.S.C. 103 have been fully considered by the examiner and are not persuasive.
Applicant argues (see pages 19 and 20 of 25 of Remarks filed 12/05/2025) that neither Sithiravel nor Ferguson teaches “calculating, first confidence of a plurality of first distance relationship values obtained based on laser point cloud data for a predetermined number of periods” and “calculating, second confidence of a plurality of second distance relationship values obtained based on scene images for the predetermined number of periods”. Applicant states that Ferguson fails to distinguish two sources of distance data (i.e. distance relationships such as laser point cloud data or scene images) or compute two separate confidences. The examiner disagrees, Ferguson teaches (see Ferguson column 4 lines 32-46) that a confidence threshold may be determined for the lane reliability information, and that the computer may take a determined confidence and compare it to the threshold for a set time period. Given that this confidence determination occurs multiple times over the duration of the vehicle’s travel, i.e. for multiple time periods, the examiner is interpreting this as being analogous to computing multiple confidences (at least a first and second) for multiple time periods (at least a first and a second) (See Ferguson, column 6, lines 44-65, where the confidence threshold may be updated or determined again iteratively and based on multiple distance scenarios). The examiner further notes that Ferguson uses LiDAR data to determine the distances between at least two objects and the self-vehicle, such as other vehicles on the road and lane markings (see Ferguson, column 3, lines 25-58) which constitutes at least one of the two sources of data as claimed. Further, Ferguson teaches that in addition to collected point cloud data, images are captured to determine characteristics of the vehicle’s environment and to be used to estimate speed and positions of objects near the vehicle (see Ferguson column 12 lines 60 through column 13 line 40, and column 15, lines 55-65) which constitutes a second source of data to generate a second distance relationship as claimed.
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Applicant further argues (see pages 20-22 of 25 of Remarks filed 12/05/2025) that Ferguson fails to teach calculating a first and second confidence based on the ideal lane change and lane keep model, “wherein the ideal lane change model represents a time-varying relationship of a distance relationship value between another vehicle in the scene around the current vehicle and the center line of the lane in which the current vehicle is located when the another vehicle changes a lane and the ideal lane keep model represents a time-varying relationship of a distance relationship value between another vehicle and the center line of the lane in which the current vehicle is located when the another vehicle moves along the lane” . Applicant argues this is in part because Ferguson fails to teach determining a “confidence” as taught by the applicant because the claimed “confidence” represents the probability of driving intention (lane keep vs lane change). Further, applicant argues that Ferguson fails to teach two distinct models, a lane keep model and a lane change model, for two distinct situations. The examiner disagrees with applicant’s assertion that the confidence taught by Ferguson is not analogous to that claimed by the applicant, and that Ferguson fails to teach two models. Ferguson teaches that the computer system may estimate the path of a leading vehicle using LiDAR point cloud data and the relationship between the leading vehicle, the self-vehicle and the lane markings based on this when all the vehicles are within the same lane (see Ferguson, column 3 lines 59-column 4 line 3), which would be analogous to the applicant’s definition of the lane keep model in paragraph [0020] of the applicant’s specification. Further, Ferguson teaches that the computer system may estimate the path of the preceding vehicle in a situation where the vehicle is changing lanes, or swerving, thereby detecting changes in this type of vehicle behavior (see Ferguson column 3 lines 59-67 and column 4 lines 4-34) which is analogous to the applicant’s definition of an ideal lane change model as described in specification [0020] of the applicant’s specification. Regarding applicant’s arguments that the confidence of the claimed invention is not taught by Ferguson, the examiner respectfully disagrees. The claims state that the confidence is based on the lane change and keep models, and distance relationships, Ferguson teaches that the confidence generated is confidence of the generated lane information, which includes the functional equivalents to the claimed lane models, as well as distance sensor information (distance relationships), (see Ferguson column 4 lines 33-44). The applicant argues that this is not sufficient to teach the claimed confidence calculations because the confidence of the claimed invention is meant to reflect the probability of a driving intention. The examiner notes that this is not reflected in the claim language, and respectfully encourages the applicant to amend to further narrow the scope of the claimed confidence calculations to distinguish the claimed invention further.
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Finally, applicant further argues (see pages 22 and 23 of 25 of Remarks filed 12/05/2025) that Schmudderich fails to remedy the above deficiencies because Schmudderich fails to teach an “ideal lane change model” and calculation of a specific “fitting degree”. The examiner respectfully disagrees, Schmudderich teaches in [0053]- [0056] teaches that the system has the capacity to determine feasibility and safety of lane changes by the self-vehicle, as well as the driving behaviors of other target vehicles in the adjacent lanes or the same lane as the self-vehicle. This is analogous to the lane keep and lane changes models as described in [0020] the presently filed application. Further, Schmudderich teaches on Page 10, [0075] the computation of 4 specific fitting degrees using lane change information, and further solving for specific parameters of the equation in determining the fitting degree. One of ordinary skill in the art would understand this as being analogous to solving for unknown parameters and determining multiple fitting degrees as claimed in the presently filed application. Therefore, for at least the reasons discussed above, the examiner maintains the rejections made the claims over Sithiravel, Ferguson, Schmudderich, Goto and in further view of Chen as fully discussed below.
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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, 4, 10, 13, 19 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Sithiravel US (20210089791 A1) in view of Ferguson (US 8504233 B1) and in further view of Schmudderich (EP 2942765 B1).
Regarding claim 1 Sithiravel discloses; A method for recognizing a vehicle lane change trend, the method comprising:
obtaining laser point cloud data of a detected target vehicle, wherein the target vehicle is a vehicle traveling in a scene around a current vehicle ([0029] Sensors may collect data related to the vehicle… sensors can detect phenomena such as … locations of neighboring vehicles, neighboring vehicles are target vehicles on the road with the current vehicle [0053] Vehicle sensor data can be a lidar point cloud);
obtaining, based on the laser point cloud data, a first distance relationship value between a center line of a lane in which the current vehicle is located and the target vehicle ([0057] Vehicles relationship with respect to the center of the roadway lane can be calculated using sensor data);
obtaining a scene image comprising the target vehicle ([0029] the sensor data may include a camera, and may collect data related to the vehicle such as neighboring vehicles and road conditions, [0057] Multiple vehicles can be selected and their respective distances from the center of the lane can be determined);
obtaining, based on the scene image, a second distance relationship value between the center line of the lane in which the current vehicle is located and the target vehicle ([0053] based upon the sensor data, the vehicle’s distance between lane markings (center lines) and other vehicles (target vehicles) can be located, since this is done multiple times, it would be analogous to a second distance relationship);
[calculating, based on an ideal lane change model and an ideal lane keep model, first confidence of a plurality of first distance relationship values obtained based on laser point cloud data for a predetermined number of periods,
wherein the ideal lane change model represents a time-varying relationship of a distance relationship value between another vehicle in the scene around the current vehicle and the center line of the lane in which the current vehicle is located when the another vehicle changes a lane,
and the ideal lane keep model represents a time-varying relationship of a distance relationship value between another vehicle and the center line of the lane in which the current vehicle is located when the another vehicle moves along the lane;
calculating, based on the ideal lane change model and the ideal lane keep model, second confidence of a plurality of second distance relationship values obtained based on scene images for the predetermined number of periods;
calculating a plurality of fusion distance relationship values of the plurality of first distance relationship values and the plurality of second distance relationship values based on the first confidence and the second confidence;
and determining, based on the plurality of fusion distance relationship values, whether the target vehicle has a lane change trend,
wherein calculating, based on the ideal lane change model and the ideal lane keep model, the first confidence of the plurality of first distance relationship values comprises:
calculating a value of each unknown parameter in the ideal lane change model based on the plurality of first distance relationship values to obtain a first available lane change model;
calculating a value of each unknown parameter in the ideal lane keep model based on the plurality of first distance relationship values to obtain a first available lane keep model;]
calculating a first fitting degree of the plurality of first distance relationship values to the first available lane change model (Sithiravel, [0019] Computing device computers B splines based on controlled points, object tracks can be determined based on locations of non-stationary objects acquired by vehicle sensors at different timesteps for a plurality of locations. B splines are a fitting equation, in this case the data being fitted is the locations or distances of non-stationary objects (neighboring vehicles), and the locations and paths of these vehicles over time, which would include lane change behavior)
and a second fitting degree of the plurality of first distance relationship values to the first available lane keep model (Sithiravel, [0019] Computing device computers B splines based on controlled points, object tracks can be determined based on locations of non-stationary objects acquired by vehicle sensors at different timesteps for a plurality of locations, this can be performed on both a first and second roadway (first and second fittings respectively). B splines are a fitting equation, in this case the data being fitted is the locations or distances of non-stationary objects (neighboring vehicles), and the locations and paths of these vehicles over time, which would include behavior of vehicles within the same lane);
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obtaining the first confidence of the plurality of first distance relationship values based on the first fitting degree and the second fitting degree (Sithiravel, [0054] data on the second roadway lanes can be determined using the vehicle path polynomials (first and second fitting degrees) and can be based vehicles on the roadway, and lane markings (distance relationships), the second roadway lanes can be used to increase the confidence in the lane data (obtaining a confidence based upon a fitting degree and distance relationships));
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[wherein calculating, based on the ideal lane change model and the ideal lane keep model, the second confidence of the plurality of second distance relationship values comprises:
calculating a value of each unknown parameter in the ideal lane change model based on the plurality of second distance relationship values to obtain a second available lane change model;
calculating a value of each unknown parameter in the ideal lane keep model based on the plurality of second distance relationship values to obtain a second available lane keep model;
calculating a third fitting degree of the plurality of second distance relationship values to the second available lane change model and a fourth fitting degree of the plurality of second distance relationship values to the second available lane keep model;
and obtaining the second confidence of the plurality of second distance relationship values based on the third fitting degree and the fourth fitting degree.]
Sithiravel does not teach; calculating, based on an ideal lane change model and an ideal lane keep model, first confidence of a plurality of first distance relationship values obtained based on laser point cloud data for a predetermined number of periods,
wherein the ideal lane change model represents a time-varying relationship of a distance relationship value between another vehicle in the scene around the current vehicle and the center line of the lane in which the current vehicle is located when the another vehicle changes a lane,
and the ideal lane keep model represents a time-varying relationship of a distance relationship value between another vehicle and the center line of the lane in which the current vehicle is located when the another vehicle moves along the lane;
calculating, based on the ideal lane change model and the ideal lane keep model, second confidence of a plurality of second distance relationship values obtained based on scene images for the predetermined number of periods;
calculating a plurality of fusion distance relationship values of the plurality of first distance relationship values and the plurality of second distance relationship values based on the first confidence and the second confidence;
and determining, based on the plurality of fusion distance relationship values, whether the target vehicle has a lane change trend,
wherein calculating, based on the ideal lane change model and the ideal lane keep model, the first confidence of the plurality of first distance relationship values comprises:
calculating a value of each unknown parameter in the ideal lane change model based on the plurality of first distance relationship values to obtain a first available lane change model;
calculating a value of each unknown parameter in the ideal lane keep model based on the plurality of first distance relationship values to obtain a first available lane keep model;
wherein calculating, based on the ideal lane change model and the ideal lane keep model, the second confidence of the plurality of second distance relationship values comprises:
calculating a value of each unknown parameter in the ideal lane change model based on the plurality of second distance relationship values to obtain a second available lane change model;
calculating a value of each unknown parameter in the ideal lane keep model based on the plurality of second distance relationship values to obtain a second available lane keep model;
calculating a third fitting degree of the plurality of second distance relationship values to the second available lane change model and a fourth fitting degree of the plurality of second distance relationship values to the second available lane keep model;
and obtaining the second confidence of the plurality of second distance relationship values based on the third fitting degree and the fourth fitting degree.
However, in the same field of endeavor Ferguson teaches;
calculating, based on an ideal lane change (Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application), Ferguson, Column 3, Lines 59-67 and column 4 lines 4-34, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, Column 4, Lines 26-32 lane information and distance information may be determined for the leading vehicle in scenarios where the vehicle is changing lanes or swerving) model and an ideal lane keep model (Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application), Ferguson, Column 3, Lines 59-67, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, this situation would apply for a vehicle traveling in the same lane as the current vehicle), first confidence of a plurality of first distance relationship values obtained based on laser point cloud data for a predetermined number of periods, (Ferguson, Column 4, Lines 32-46, The lane confidence can be calculated, which the system’s confidence in the lane information calculated which may be calculated for a predetermined time period, Examiner is interpreting this as being analogous to the first confidence)
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wherein the ideal lane change model represents a time-varying relationship of a distance relationship value between another vehicle in the scene around the current vehicle and the center line of the lane in which the current vehicle is located when the another vehicle changes a lane (Ferguson, Column 3, Lines 59-67 and column 4 lines 4-34, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, Column 4, Lines 26-32 lane information and distance information may be determined for the leading vehicle in scenarios where the vehicle is changing lanes or swerving)),
and the ideal lane keep model represents a time-varying relationship of a distance relationship value between another vehicle and the center line of the lane in which the current vehicle is located when the another vehicle moves along the lane (Ferguson, Column 3, Lines 59-67, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, this situation would apply for a vehicle traveling in the same lane as the current vehicle);
calculating, based on an ideal lane change model (Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application), Ferguson, Column 3, Lines 59-67 and column 4 lines 4-34, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, Column 4, Lines 26-32 lane information and distance information may be determined for the leading vehicle in scenarios where the vehicle is changing lanes or swerving) and an ideal lane keep model (Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application), Ferguson, Column 3, Lines 59-67, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, this situation would apply for a vehicle traveling in the same lane as the current vehicle), second confidence of a plurality of second distance relationship values obtained based on scene images for the predetermined number of periods (Ferguson, Column 6, Lines 44-65, the vehicles distance information and distances to other vehicles can be used to updated the confidence threshold (Second confidence threshold), Column 4 lines 39-46 in some embodiments the lane reliability information may be determined for set time periods);
calculating a plurality of fusion distance relationship values of the plurality of first distance relationship values and the plurality of (Ferguson, Column 15, Lines 40-55, a sensor fusion algorithm may be used to fuse data from the sensor (which includes the distance sensors), creates multiple fusion assessments which are being interpreted as being analogous to a plurality of fusion distance relationships);
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and determining, based on the plurality of fusion distance relationship values, whether the target vehicle has a lane change trend (Ferguson, Column 15, Lines 47-55, the fusion assessments may be used to determine changes/evaluations of objects, situations and the vehicles environments, Column 15 lines 66-67 and Column 16 lines 1-6 the vehicles positioning and driving path can be determined using the sensor fusion data which would include lane changes).
wherein calculating, based on the ideal lane change model (Ferguson, Column 3, Lines 59-67 and column 4 lines 4-34, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, Column 4, Lines 26-32 lane information and distance information may be determined for the leading vehicle in scenarios where the vehicle is changing lanes or swerving, Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application)) and an ideal lane keep model (Ferguson, Column 3, Lines 59-67, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, this situation would apply for a vehicle traveling in the same lane as the current vehicle, Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application), the first confidence of the plurality of first distance relationship values comprises (Ferguson, Column 3, Lines 40-58, Lane information may be determined from a location of a vehicle on a road, or distances of a plurality of vehicles on the road, meaning the lane information is generated from a distance relationship, Ferguson, Column 4, Lines 33-46, In some embodiments the system may determine that the confidence of the lane information is below a certain threshold, (first confidence of a distance relationship), Ferguson, Column 6, Lines 44-65, once the lane information has been determined to be unreliable, the information can be updated to get a confidence of the updated lane information (second confidence of a distance relationship)):
calculating a value of each unknown parameter in the ideal lane change model based on the plurality of first distance relationship values to obtain a first available lane change model (Ferguson, Column 2, Lines 42-67, Upon determining that the vehicle information is unreliable or unknown, the vehicle may use distance relationships between the vehicle and other vehicles in other lanes to determine the position of the other vehicle and maintain a minimum distance from the other moving vehicle);
calculating a value of each unknown parameter in the ideal lane keep model based on the plurality of first distance relationship values to obtain a first available lane keep model (Ferguson, Column 2, Lines 27-46, In the event a vehicle is unable to determine lane information, the sensors may use data from vehicles next to or behind the vehicle, which in turn allows the system to obtain vehicle environment information based upon surround vehicles positions/distances);
wherein calculating, based on the ideal lane change model (Ferguson, Column 3, Lines 59-67, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, Column 4, Lines 26-32 lane information and distance information may be determined for the leading vehicle in scenarios where the vehicle is changing lanes or swerving, Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application)) and an ideal lane keep model (Ferguson, Column 3, Lines 59-67, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, this situation would apply for a vehicle traveling in the same lane as the current vehicle, Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application), the second confidence of the plurality of second distance relationship values comprises (Ferguson, Column 3, Lines 40-58, Lane information may be determined from a location of a vehicle on a road, or distances of a plurality of vehicles on the road, meaning the lane information is generated from a distance relationship, Ferguson, Column 4, Lines 33-46, In some embodiments the system may determine that the confidence of the lane information is below a certain threshold, (first confidence of a distance relationship), Ferguson, Column 6, Lines 44-65, once the lane information has been determined to be unreliable, the information can be updated to get a confidence of the updated lane information (second confidence of a distance relationship)):
calculating a value of each unknown parameter in the ideal lane change model based on the plurality of second distance relationship values to obtain a second available lane change model (Ferguson, Column 2, Lines 42-67, Upon determining that the vehicle information is unreliable or unknown, the vehicle may use distance relationships between the vehicle and other vehicles in other lanes to determine the position of the other vehicle and maintain a minimum distance from the other moving vehicle. Column 4, Lines 60-67, and Column 5 lines 1-3, Once unreliable or unknown information about the environment is detected the vehicle may detect the positions of at least 2 other moving objects in a predetermined distance from the vehicle based upon lane width, therefore there must be a second distance relationship used to determine unknown information);
calculating a value of each unknown parameter in the ideal lane keep model based on the plurality of second distance relationship values to obtain a second available lane keep model (Ferguson, Column 5, lines 4-17, when lane information becomes unreliable, the vehicle may use the information for at least one neighboring vehicle and move the vehicle to maintain a defined distance within the lane. Ferguson Column 5, lines 32-43, in some cases “at least one vehicle” may refer to a first neighboring vehicle and a second. Indicating the use of a second distance relationship used in determining the lane keep model (the relationship between, the vehicle, the lane markings and another vehicle in the same lane));
The combination of Sithiravel and Ferguson would have been obvious to one of ordinary skill in the art prior to the filing date of the presently claimed invention. The motivation for the combination of the fusion sensor system of Ferguson with the system of Sithiravel would improve the system by allowing the system to detect changes in the driving situation and prevent safety issues arising from them. Further, the combination would allow for the lane information and lane confidences may be updated based upon changes in the vehicle’s environment and the vehicles on the road over time to maintain accurate information. (Ferguson, Column 4, Lines 44-65, Column 15)
The combination of Sithiravel and Ferguson does not teach; calculating a third fitting degree of the plurality of second distance relationship values to the second available lane change model and a fourth fitting degree of the plurality of second distance relationship values to the second available lane keep model;
and obtaining the second confidence of the plurality of second distance relationship values based on the third fitting degree and the fourth fitting degree.
However, in the same field of endeavor, Schmudderich teaches;
calculating a third fitting degree of the plurality of second distance relationship values to the second available lane change model (Schmudderich, Page 10, Line 25, Fitting left gap (First fitting degree), Page 10, line 55, fitting right gap (second fitting degree), Page 11, Lines 10-15, Fitting left lane (third fitting degree) is based upon the vehicles time to collision (TTC), [0038]which uses distance relationships to other vehicles, fitting the left lane using the TTC is a third fitting degree using a plurality of obtained distance relationships, [0053] the lane fittings are used to determine the feasibility of lane changes left or lane changes right using the prediction model) and a fourth fitting degree of the plurality of second distance relationship values to the second available lane keep model (Schmudderich, Page 11, Lines 18-20, Fitting current lane (Fourth fitting degree), is based upon the vehicles time to collision (TTC), [0038] which uses distance relationships to other vehicles, therefore this a is a fourth fitting degree based upon a plurality of distance relationships, [0038], The TTC and gap/lane fitting can be used to determine if changing lanes is feasible or not at the moment based upon the distance relationships (Lane keep modeling));
and obtaining the second confidence of the plurality of second distance relationship values based on the third fitting degree and the fourth fitting degree (Schmudderich, [0074] Confidence is based upon sensor elements and indicators, the indicators being the gap and lane fitting parameters, as described on pages 10 and 11, including the Fitting of the left lane and the fitting of the current lane (third and fourth fitting degree respectively).
The combination of Sithiravel, Ferguson and Schmudderich would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The combination of Sithiravel and Ferguson teaches a system of determining vehicle lane environments, distance relationships and the behavior of the vehicle’s surrounding it with fitting and confidence values. Schmudderich teaches, a system of determining whether an autonomous vehicle can change lanes based upon lane fitting data. The motivation for the combination lies in that using the fitting models of Schmudderich with the system of Ferguson and Sithiravel would improve the vehicle’s ability to assess lane change availability. (Schmudderich, [0034], [0038], [0054] and pages 10 and 11, see equations listed on pages 10 and 11)
Regarding claim 4 The combination of Sithiravel, Ferguson and Schmudderich teaches; The method according to claim 1, wherein the obtaining, based on the scene image (Sithiravel, [0057] Traffic scene 1100), a second distance relationship value between the center line of the lane in which the current vehicle is located and the target vehicle comprises (Sithiravel, [0057] the relationship between the target vehicle(s) is determined based upon their relationship to the center of their respective roadway lanes):
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calculating, in an image coordinate system of the scene image (Sithiravel, [0060] a second map is determined… the second map is a vehicle coordinate system which can be used to determine roadway markings), a vertical distance between the target vehicle and the center line of the lane in which the current vehicle is located, wherein the vertical distance is determined based on a difference between a vertical coordinate of the target vehicle and a vertical coordinate of the center line of the lane in which the current vehicle is located; (Sithiravel, [0028] sensors determine a distance between a vehicle and the vehicle in front of it (which is being interpreted as the vertical distance), this is determined using vehicle location coordinates);
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calculating a width of the lane in which the current vehicle is located in the image coordinate system of the scene image (Sithiravel, [0054] Lane width is used in computed the vehicle path polynomials; therefore, the vehicle lane widths must be computed);
calculating a second ratio of the width of the lane in which the current vehicle is located in the image coordinate system to the vertical distance (Sithiravel, [0043] lateral and longitudinal coordinates of the vehicle are calculated to obtain the lateral and longitudinal distances of the vehicles relative to the roadway and lane markings, this is used in generating the vehicle’s path polynomials, [0054] Vehicle lane with is computed based upon roadway markings, [0057] Vehicle paths polynomials are determined based upon the vehicle’s paths and the roadway lanes. Examiner is interpreting the calculation of vehicle polynomials as the ratio utilizing the lane widths, and distances between vehicles on the roadway because this calculation uses a B spline computation which is defined by a ratio of coefficients, in this case, the data used is for the vehicle positions and lane markings);
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and determining, the second distance relationship value as the second distance ratio (Sithiravel, [0043] lateral and longitudinal coordinates of the vehicle are calculated to obtain the lateral and longitudinal distances of the vehicles relative to the roadway and lane markings, this is used in generating the vehicle’s path polynomials, [0057] Vehicle paths polynomials are determined based upon the vehicle’s paths and the roadway lanes. Examiner is interpreting the vehicle polynomials as the distance relationship utilizing the land widths, and distances between vehicles on the roadway).
Regarding claim 10, the combination of Sithiravel, Ferguson and Schmudderich teaches;
An apparatus for recognizing a vehicle lane change trend, wherein the apparatus comprises:
at least one processor (Sithiravel, [0025] instructions are stored in a memory and executed by processors);
and one or more memories coupled to the at least one processor and storing programming instructions for execution by the at least one processor, to perform comprising (Sithiravel, [0025] instructions are stored in a memory and executed by processors):
obtaining laser point cloud data of a detected target vehicle, wherein the target vehicle is a vehicle traveling in a scene around a current vehicle (Sithiravel, [0029] Sensors may collect data related to the vehicle… sensors can detect phenomena such as … locations of neighboring vehicles [0053] Vehicle sensor data can be a lidar point cloud);
obtaining, based on the laser point cloud data, a first distance relationship value between a center line of a lane in which the current vehicle is located and the target vehicle (Sithiravel, [0057] Vehicles relationship with respect to the center of the roadway lane can be calculated using sensor data);
obtaining a scene image comprising the target vehicle (Sithiravel, [0029] the sensor data may include a camera, and may collect data related to the vehicle such as neighboring vehicles and road conditions, [0057] Multiple vehicles can be selected and their respective distances from the center of the lane can be determined);
obtaining, based on the scene image, a second distance relationship value between the center line of the lane in which the current vehicle is located and the target vehicle (Sithiravel, [0053] based upon the sensor data, the vehicle’s distance between lane markings (center lines) and other vehicles (target vehicles) can be located, since this is done multiple times, it would be analogous to a second distance relationship);
calculating, based on an ideal lane change (Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application), Ferguson, Column 3, Lines 59-67 and column 4 lines 4-34, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, Column 4, Lines 26-32 lane information and distance information may be determined for the leading vehicle in scenarios where the vehicle is changing lanes or swerving) model and an ideal lane keep model (Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application), Ferguson, Column 3, Lines 59-67, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, this situation would apply for a vehicle traveling in the same lane as the current vehicle), first confidence of a plurality of first distance relationship values obtained based on laser point cloud data for a predetermined number of periods, (Ferguson, Column 4, Lines 32-46, The lane confidence can be calculated, which the system’s confidence in the lane information calculated which may be calculated for a predetermined time period, Examiner is interpreting this as being analogous to the first confidence)
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wherein the ideal lane change model represents a time-varying relationship of a distance relationship value between another vehicle in the scene around the current vehicle and the center line of the lane in which the current vehicle is located when the another vehicle changes a lane (Ferguson, Column 3, Lines 59-67 and column 4 lines 4-34, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, Column 4, Lines 26-32 lane information and distance information may be determined for the leading vehicle in scenarios where the vehicle is changing lanes or swerving)),
and the ideal lane keep model represents a time-varying relationship of a distance relationship value between another vehicle and the center line of the lane in which the current vehicle is located when the another vehicle moves along the lane (Ferguson, Column 3, Lines 59-67, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, this situation would apply for a vehicle traveling in the same lane as the current vehicle);
calculating, based on an ideal lane change (Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application), Ferguson, Column 3, Lines 59-67 and column 4 lines 4-34, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, Column 4, Lines 26-32 lane information and distance information may be determined for the leading vehicle in scenarios where the vehicle is changing lanes or swerving) model and an ideal lane keep model (Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application), Ferguson, Column 3, Lines 59-67, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, this situation would apply for a vehicle traveling in the same lane as the current vehicle), second confidence of a plurality of (Ferguson, Column 6, Lines 44-65, the vehicles distance information and distances to other vehicles can be used to updated the confidence threshold (Second confidence threshold), Column 4 lines 39-46 in some embodiments the lane reliability information may be determined for set time periods);
calculating a plurality of fusion distance relationship values of the plurality of (Ferguson, Column 15, Lines 40-55, a sensor fusion algorithm may be used to fuse data from the sensor (which includes the distance sensors), creates multiple fusion assessments which are being interpreted as being analogous to a plurality of fusion distance relationships);
and determining, based on the plurality of fusion distance relationship values, whether the target vehicle has a lane change trend (Ferguson, Column 15, Lines 47-55, the fusion assessments may be used to determine changes/evaluations of objects, situations and the vehicles environments, Column 15 lines 66-67 and Column 16 lines 1-6 the vehicles positioning and driving path can be determined using the sensor fusion data which would include lane changes),
wherein the operations further comprise;
calculating a value of each unknown parameter in the ideal lane change model based on the plurality of first distance relationship values to obtain a first available lane change model (Ferguson, Column 2, Lines 42-67, Upon determining that the vehicle information is unreliable or unknown, the vehicle may use distance relationships between the vehicle and other vehicles in other lanes to determine the position of the other vehicle and maintain a minimum distance from the other moving vehicle);
calculating a value of each unknown parameter in the ideal lane keep model based on the plurality of first distance relationship values to obtain a first available lane keep model (Ferguson, Column 2, Lines 27-46, In the event a vehicle is unable to determine lane information, the sensors may use data from vehicles next to or behind the vehicle, which in turn allows the system to obtain vehicle environment information based upon surround vehicles positions/distances);
calculating a first fitting degree of the plurality of first distance relationship values to the first available lane change model (Sithiravel, [0019] Computing device computers B splines based on controlled points, object tracks can be determined based on locations of non-stationary objects acquired by vehicle sensors at different timesteps for a plurality of locations. B splines are a fitting equation, in this case the data being fitted is the locations or distances of non-stationary objects (neighboring vehicles), and the locations and paths of these vehicles over time, which would include lane change behavior)
and a second fitting degree of the plurality of first distance relationship values to the first available lane keep model (Sithiravel, [0019] Computing device computers B splines based on controlled points, object tracks can be determined based on locations of non-stationary objects acquired by vehicle sensors at different timesteps for a plurality of locations, this can be performed on both a first and second roadway (first and second fittings respectively). B splines are a fitting equation, in this case the data being fitted is the locations or distances of non-stationary objects (neighboring vehicles), and the locations and paths of these vehicles over time, which would include behavior of vehicles within the same lane);
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obtaining the first confidence of the plurality of first distance relationship values based on the first fitting degree and the second fitting degree (Sithiravel, [0054] data on the second roadway lanes can be determined using the vehicle path polynomials (first and second fitting degrees) and can be based vehicles on the roadway, and lane markings (distance relationships), the second roadway lanes can be used to increase the confidence in the lane data (obtaining a confidence based upon a fitting degree and distance relationships);
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wherein calculating, based on the ideal lane change model (Ferguson, Column 3, Lines 59-67 and column 4 lines 4-34, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, Column 4, Lines 26-32 lane information and distance information may be determined for the leading vehicle in scenarios where the vehicle is changing lanes or swerving, Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application)) and an ideal lane keep model (Ferguson, Column 3, Lines 59-67, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, this situation would apply for a vehicle traveling in the same lane as the current vehicle, Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application) the second confidence of the plurality of second distance relationship values comprises (Ferguson, Column 3, Lines 40-58, Lane information may be determined from a location of a vehicle on a road, or distances of a plurality of vehicles on the road, meaning the lane information is generated from a distance relationship, Ferguson, Column 4, Lines 33-46, In some embodiments the system may determine that the confidence of the lane information is below a certain threshold, (first confidence of a distance relationship), Ferguson, Column 6, Lines 44-65, once the lane information has been determined to be unreliable, the information can be updated to get a confidence of the updated lane information (second confidence of a distance relationship):
calculating a value of each unknown parameter in the ideal lane change model based on the plurality of second distance relationship values to obtain a second available lane change model (Ferguson, Column 2, Lines 42-67, Upon determining that the vehicle information is unreliable or unknown, the vehicle may use distance relationships between the vehicle and other vehicles in other lanes to determine the position of the other vehicle and maintain a minimum distance from the other moving vehicle. Column 4, Lines 60-67, and Column 5 lines 1-3, Once unreliable or unknown information about the environment is detected the vehicle may detect the positions of at least 2 other moving objects in a predetermined distance from the vehicle based upon lane width, therefore there must be a second distance relationship used to determine unknown information);
calculating a value of each unknown parameter in the ideal lane keep model based on the plurality of second distance relationship values to obtain a second available lane keep model (Ferguson, Column 5, lines 4-17, when lane information becomes unreliable, the vehicle may use the information for at least one neighboring vehicle and move the vehicle to maintain a defined distance within the lane. Ferguson Column 5, lines 32-43, in some cases “at least one vehicle” may refer to a first neighboring vehicle and a second. Indicating the use of a second distance relationship used in determining the lane keep model (the relationship between, the vehicle, the lane markings and another vehicle in the same lane));
The combination of Sithiravel and Ferguson would have been obvious to one of ordinary skill in the art prior to the filing date of the presently claimed invention. The motivation for the combination of the fusion sensor system of Ferguson with the system of Sithiravel would improve the system by allowing the system to detect changes in the driving situation and prevent safety issues arising from them. Further, the combination would allow for the lane information and lane confidences may be updated based upon changes in the vehicle’s environment and the vehicles on the road over time to maintain accurate information. (Ferguson, Column 4, Lines 44-65, Column 15)
calculating a third fitting degree of the plurality of second distance relationship values to the second available lane change model (Schmudderich, Page 10, Line 25, Fitting left gap (First fitting degree), Page 10, line 55, fitting right gap (second fitting degree), Page 11, Lines 10-15, Fitting left lane (third fitting degree) is based upon the vehicles time to collision (TTC), [0038]which uses distance relationships to other vehicles, fitting the left lane using the TTC is a third fitting degree using a plurality of obtained distance relationships, [0053] the lane fittings are used to determine the feasibility of lane changes left or lane changes right using the prediction model) and a fourth fitting degree of the plurality of second distance relationship values to the second available lane keep model (Schmudderich, Page 11, Lines 18-20, Fitting current lane (Fourth fitting degree), is based upon the vehicles time to collision (TTC), [0038] which uses distance relationships to other vehicles, therefore this a is a fourth fitting degree based upon a plurality of distance relationships, [0038], The TTC and gap/lane fitting can be used to determine if changing lanes is feasible or not at the moment based upon the distance relationships (Lane keep modeling));
and obtaining the second confidence of the plurality of second distance relationship values based on the third fitting degree and the fourth fitting degree (Schmudderich, [0074] Confidence is based upon sensor elements and indicators, the indicators being the gap and lane fitting parameters, as described on pages 10 and 11, including the Fitting of the left lane and the fitting of the current lane (third and fourth fitting degree respectively).
The combination of Sithiravel, Ferguson and Schmudderich would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The combination of Sithiravel and Ferguson teaches a system of determining vehicle lane environments, distance relationships and the behavior of the vehicle’s surrounding it with fitting and confidence values. Schmudderich teaches, a system of determining whether an autonomous vehicle can change lanes based upon lane fitting data. The motivation for the combination lies in that using the fitting models of Schmudderich with the system of Ferguson and Sithiravel would improve the vehicle’s ability to assess lane change availability. (Schmudderich, [0034], [0038], [0054] and pages 10 and 11, see equations listed on pages 10 and 11)
Regarding claim 13, the combination of Sithiravel, Ferguson and Schmudderich teaches; The apparatus according to claim 10, wherein the one or more memories store the programming instructions for execution by the at least one processor to perform the following operation (Sithiravel, [0025] instructions are stored in a memory and executed by processors):
calculating, in an image coordinate system of the scene image(Sithiravel, [0057] Traffic scene 1100), (Sithiravel, [0060] a second map is determined… the second map is a vehicle coordinate system which can be used to determine roadway markings), a vertical distance between the target vehicle and the center line of the lane in which the current vehicle is located, wherein the vertical distance is determined based on a difference between a vertical coordinate of the target vehicle and a vertical coordinate of the center line of the lane in which the current vehicle is located (Sithiravel, [0028] sensors determine a distance between a vehicle and the vehicle in front of it (which is being interpreted as the vertical distance), this is determined using vehicle location coordinates);
calculating a width of the lane in which the current vehicle is located in the image coordinate system of the scene image (Sithiravel, [0054] Lane width is used in computed the vehicle path polynomials, therefore the vehicle lane widths must be computed);
calculating a second ratio of the vertical distance to the width of the lane in which the current vehicle is located in the image coordinate system of the scene image (Sithiravel, [0043] lateral and longitudinal coordinates of the vehicle are calculated to obtain the lateral and longitudinal distances of the vehicles relative to the roadway and lane markings, this is used in generating the vehicle’s path polynomials, [0054] Vehicle lane with is computed based upon roadway markings, [0057] Vehicle paths polynomials are determined based upon the vehicle’s paths and the roadway lanes. Examiner is interpreting the calculation of vehicle polynomials as the ratio utilizing the lane widths, and distances between vehicles on the roadway because this calculation uses a B spline computation which is defined by a ratio of coefficients, in this case, the data used is for the vehicle positions and lane markings);
and determining, the second distance relationship value as the second ratio (Sithiravel, [0043] lateral and longitudinal coordinates of the vehicle are calculated to obtain the lateral and longitudinal distances of the vehicles relative to the roadway and lane markings, this is used in generating the vehicle’s path polynomials, [0057] Vehicle paths polynomials are determined based upon the vehicle’s paths and the roadway lanes. Examiner is interpreting the vehicle polynomials as the distance relationship utilizing the land widths, and distances between vehicles on the roadway).
Regarding claim 19, the combination of Sithiravel, Ferguson and Schmudderich teaches;
non-transitory computer-readable storage medium storing programming instructions for execution by at least one processor to perform operations comprising (Sithiravel, [0021] The computing device 115 includes a processor and a memory such as are known. Further, the memory includes one or more forms of computer-readable media, and stores instructions executable by the processor for performing various operations, including as disclosed herein):
obtaining laser point cloud data of a detected target vehicle, wherein the target vehicle is a vehicle traveling in a scene around a current vehicle (Sithiravel, [0029] Sensors may collect data related to the vehicle… sensors can detect phenomena such as … locations of neighboring vehicles [0053] Vehicle sensor data can be a lidar point cloud);
obtaining, based on the laser point cloud data, a first distance relationship value between a center line of a lane in which the current vehicle is located and the target vehicle (Sithiravel, [0057] Vehicle’s relationship with respect to the center of the roadway lane can be calculated using sensor data);
obtaining a scene image comprising the target vehicle (Sithiravel, [0029] the sensor data may include a camera, and may collect data related to the vehicle such as neighboring vehicles and road conditions, [0057] Multiple vehicles can be selected and their respective distances from the center of the lane can be determined);
obtaining, based on the scene image, a second distance relationship value between the center line of the lane in which the current vehicle is located and the target vehicle (Sithiravel, [0053] based upon the sensor data, the vehicle’s distance between lane markings (center lines) and other vehicles (target vehicles) can be located, since this is done multiple times, it would be analogous to a second distance relationship);
calculating, based on an ideal lane change (Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application), Ferguson, Column 3, Lines 59-67 and column 4 lines 4-34, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, Column 4, Lines 26-32 lane information and distance information may be determined for the leading vehicle in scenarios where the vehicle is changing lanes or swerving) model and an ideal lane keep model (Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application), Ferguson, Column 3, Lines 59-67, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, this situation would apply for a vehicle traveling in the same lane as the current vehicle), first confidence of a plurality of first distance relationship values obtained based on laser point cloud data for a predetermined number of periods, (Ferguson, Column 4, Lines 32-46, The lane confidence can be calculated, which the system’s confidence in the lane information calculated which may be calculated for a predetermined time period, Examiner is interpreting this as being analogous to the first confidence)
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wherein the ideal lane change model represents a time-varying relationship of a distance relationship value between another vehicle in the scene around the current vehicle and the center line of the lane in which the current vehicle is located when the another vehicle changes a lane (Ferguson, Column 3, Lines 59-67 and column 4 lines 4-34, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, Column 4, Lines 26-32 lane information and distance information may be determined for the leading vehicle in scenarios where the vehicle is changing lanes or swerving)),
and the ideal lane keep model represents a time-varying relationship of a distance relationship value between another vehicle and the center line of the lane in which the current vehicle is located when the another vehicle moves along the lane (Ferguson, Column 3, Lines 59-67, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, this situation would apply for a vehicle traveling in the same lane as the current vehicle);
calculating, based on an ideal lane change model (Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application), Ferguson, Column 3, Lines 59-67 and column 4 lines 4-34, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, Column 4, Lines 26-32 lane information and distance information may be determined for the leading vehicle in scenarios where the vehicle is changing lanes or swerving) model and an ideal lane keep model (Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application), Ferguson, Column 3, Lines 59-67, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, this situation would apply for a vehicle traveling in the same lane as the current vehicle), second confidence of a plurality of second distance relationship values obtained based on scene images for the predetermined number of periods (Ferguson, Column 6, Lines 44-65, the vehicles distance information and distances to other vehicles can be used to updated the confidence threshold (Second confidence threshold), Column 4 lines 39-46 in some embodiments the lane reliability information may be determined for set time periods);
calculating a plurality of fusion distance relationship values of the plurality of first distance relationship values and the plurality of second distance relationship values based on the first confidence and the second confidence (Ferguson, Column 15, Lines 40-55, a sensor fusion algorithm may be used to fuse data from the sensor (which includes the distance sensors), creates multiple fusion assessments which are being interpreted as being analogous to a plurality of fusion distance relationships);
and determining, based on the plurality of fusion distance relationship values, whether the target vehicle has a lane change trend (Ferguson, Column 15, Lines 47-55, the fusion assessments may be used to determine changes/evaluations of objects, situations and the vehicles environments, Column 15 lines 66-67 and Column 16 lines 1-6 the vehicles positioning and driving path can be determined using the sensor fusion data which would include lane changes),
wherein calculating, based on the ideal lane change model (Ferguson, Column 3, Lines 59-67 and column 4 lines 4-34, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, Column 4, Lines 26-32 lane information and distance information may be determined for the leading vehicle in scenarios where the vehicle is changing lanes or swerving, Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application)) and an ideal lane keep model (Ferguson, Column 3, Lines 59-67, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, this situation would apply for a vehicle traveling in the same lane as the current vehicle, Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application), the first confidence of the plurality of first distance relationship values comprises (Ferguson, Column 3, Lines 40-58, Lane information may be determined from a location of a vehicle on a road, or distances of a plurality of vehicles on the road, meaning the lane information is generated from a distance relationship, Ferguson, Column 4, Lines 33-46, In some embodiments the system may determine that the confidence of the lane information is below a certain threshold, (first confidence of a distance relationship), Ferguson, Column 6, Lines 44-65, once the lane information has been determined to be unreliable, the information can be updated to get a confidence of the updated lane information (second confidence of a distance relationship)):
calculating a value of each unknown parameter in the ideal lane change model based on the plurality of first distance relationship values to obtain a first available lane change model (Ferguson, Column 2, Lines 42-67, Upon determining that the vehicle information is unreliable or unknown, the vehicle may use distance relationships between the vehicle and other vehicles in other lanes to determine the position of the other vehicle and maintain a minimum distance from the other moving vehicle);
calculating a value of each unknown parameter in the ideal lane keep model based on the plurality of first distance relationship values to obtain a first available lane keep model (Ferguson, Column 2, Lines 27-46, In the event a vehicle is unable to determine lane information, the sensors may use data from vehicles next to or behind the vehicle, which in turn allows the system to obtain vehicle environment information based upon surround vehicles positions/distances);
calculating a first fitting degree of the plurality of first distance relationship values to the first available lane change model (Sithiravel, [0019] Computing device computers B splines based on controlled points, object tracks can be determined based on locations of non-stationary objects acquired by vehicle sensors at different timesteps for a plurality of locations. B splines are a fitting equation, in this case the data being fitted is the locations or distances of non-stationary objects (neighboring vehicles), and the locations and paths of these vehicles over time, which would include lane change behavior)
and a second fitting degree of the plurality of first distance relationship values to the first available lane keep model (Sithiravel, [0019] Computing device computers B splines based on controlled points, object tracks can be determined based on locations of non-stationary objects acquired by vehicle sensors at different timesteps for a plurality of locations, this can be performed on both a first and second roadway (first and second fittings respectively). B splines are a fitting equation, in this case the data being fitted is the locations or distances of non-stationary objects (neighboring vehicles), and the locations and paths of these vehicles over time, which would include behavior of vehicles within the same lane);
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obtaining the first confidence of the plurality of first distance relationship values based on the first fitting degree and the second fitting degree (Sithiravel, [0054] data on the second roadway lanes can be determined using the vehicle path polynomials (first and second fitting degrees) and can be based vehicles on the roadway, and lane markings (distance relationships), the second roadway lanes can be used to increase the confidence in the lane data (obtaining a confidence based upon a fitting degree and distance relationships);
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wherein calculating, based on the ideal lane change model (Ferguson, Column 3, Lines 59-67 and column 4 lines 4-34, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, Column 4, Lines 26-32 lane information and distance information may be determined for the leading vehicle in scenarios where the vehicle is changing lanes or swerving, Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application)) and an ideal lane keep model (Ferguson, Column 3, Lines 59-67, The computer system may estimate a path of the leading vehicle using, for example, a laser rangefinder and/or a LIDAR unit… Once the computer system has estimated the path of the leading vehicle, the computer system may estimate the location of the lane based on the estimated path, this situation would apply for a vehicle traveling in the same lane as the current vehicle, Applicant defined ideal lane change model as time-varying relationship of the distance between the current vehicle and another vehicle, and the lane center (see specification [0020] of the presently filed application) the second confidence of the plurality of second distance relationship values comprises (Ferguson, Column 3, Lines 40-58, Lane information may be determined from a location of a vehicle on a road, or distances of a plurality of vehicles on the road, meaning the lane information is generated from a distance relationship, Ferguson, Column 4, Lines 33-46, In some embodiments the system may determine that the confidence of the lane information is below a certain threshold, (first confidence of a distance relationship), Ferguson, Column 6, Lines 44-65, once the lane information has been determined to be unreliable, the information can be updated to get a confidence of the updated lane information (second confidence of a distance relationship):
calculating a value of each unknown parameter in the ideal lane change model based on the plurality of second distance relationship values to obtain a second available lane change model (Ferguson, Column 2, Lines 42-67, Upon determining that the vehicle information is unreliable or unknown, the vehicle may use distance relationships between the vehicle and other vehicles in other lanes to determine the position of the other vehicle and maintain a minimum distance from the other moving vehicle. Column 4, Lines 60-67, and Column 5 lines 1-3, Once unreliable or unknown information about the environment is detected the vehicle may detect the positions of at least 2 other moving objects in a predetermined distance from the vehicle based upon lane width, therefore there must be a second distance relationship used to determine unknown information);
calculating a value of each unknown parameter in the ideal lane keep model based on the plurality of second distance relationship values to obtain a second available lane keep model (Ferguson, Column 5, lines 4-17, when lane information becomes unreliable, the vehicle may use the information for at least one neighboring vehicle and move the vehicle to maintain a defined distance within the lane. Ferguson Column 5, lines 32-43, in some cases “at least one vehicle” may refer to a first neighboring vehicle and a second. Indicating the use of a second distance relationship used in determining the lane keep model (the relationship between, the vehicle, the lane markings and another vehicle in the same lane));
The combination of Sithiravel and Ferguson would have been obvious to one of ordinary skill in the art prior to the filing date of the presently claimed invention. The motivation for the combination of the fusion sensor system of Ferguson with the system of Sithiravel would improve the system by allowing the system to detect changes in the driving situation and prevent safety issues arising from them. Further, the combination would allow for the lane information and lane confidences may be updated based upon changes in the vehicle’s environment and the vehicles on the road over time to maintain accurate information. (Ferguson, Column 4, Lines 44-65, Column 15)
calculating a third fitting degree of the plurality of second distance relationship values to the second available lane change model (Schmudderich, Page 10, Line 25, Fitting left gap (First fitting degree), Page 10, line 55, fitting right gap (second fitting degree), Page 11, Lines 10-15, Fitting left lane (third fitting degree) is based upon the vehicles time to collision (TTC), [0038]which uses distance relationships to other vehicles, fitting the left lane using the TTC is a third fitting degree using a plurality of obtained distance relationships, [0053] the lane fittings are used to determine the feasibility of lane changes left or lane changes right using the prediction model) and a fourth fitting degree of the plurality of second distance relationship values to the second available lane keep model (Schmudderich, Page 11, Lines 18-20, Fitting current lane (Fourth fitting degree), is based upon the vehicles time to collision (TTC), [0038] which uses distance relationships to other vehicles, therefore this a is a fourth fitting degree based upon a plurality of distance relationships, [0038], The TTC and gap/lane fitting can be used to determine if changing lanes is feasible or not at the moment based upon the distance relationships (Lane keep modeling));
and obtaining the second confidence of the plurality of second distance relationship values based on the third fitting degree and the fourth fitting degree (Schmudderich, [0074] Confidence is based upon sensor elements and indicators, the indicators being the gap and lane fitting parameters, as described on pages 10 and 11, including the Fitting of the left lane and the fitting of the current lane (third and fourth fitting degree respectively).
The combination of Sithiravel, Ferguson and Schmudderich would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The combination of Sithiravel and Ferguson teaches a system of determining vehicle lane environments, distance relationships and the behavior of the vehicle’s surrounding it with fitting and confidence values. Schmudderich teaches, a system of determining whether an autonomous vehicle can change lanes based upon lane fitting data. The motivation for the combination lies in that using the fitting models of Schmudderich with the system of Ferguson and Sithiravel would improve the vehicle’s ability to assess lane change availability. (Schmudderich, [0034], [0038], [0054] and pages 10 and 11, see equations listed on pages 10 and 11)
Regarding claim 22 the combination of Sithiravel, Ferguson and Schmudderich teaches; The non-transitory computer-readable storage medium according to claim 19,
Wherein the obtaining, based on the scene image (Sithiravel, [0057] Traffic scene 1100), a second distance relationship value between the center line of the lane in which the current vehicle is located and the target vehicle comprises (Sithiravel, [0057] the relationship between the target vehicle(s) is determined based upon their relationship to the center of their respective roadway lanes):
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(Sithiravel, [0057] Emphasis added)
calculating, in an image coordinate system of the scene image (Sithiravel, [0060] a second map is determined… the second map is a vehicle coordinate system which can be used to determine roadway markings), a vertical distance between the target vehicle and the center line of the lane in which the current vehicle is located, wherein the vertical distance is determined based on a difference between a vertical coordinate of the target vehicle and a vertical coordinate of the center line of the lane in which the current vehicle is located; (Sithiravel, [0028] sensors determine a distance between a vehicle and the vehicle in front of it (which is being interpreted as the vertical distance), this is determined using vehicle location coordinates);
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(Sithiravel [0028], emphasis added)
calculating a width of the lane in which the current vehicle is located in the image coordinate system of the scene image (Sithiravel, [0054] Lane width is used in computed the vehicle path polynomials; therefore, the vehicle lane widths must be computed);
calculating a second ratio of the width of the lane in which the current vehicle is located in the image coordinate system to the vertical distance (Sithiravel, [0043] lateral and longitudinal coordinates of the vehicle are calculated to obtain the lateral and longitudinal distances of the vehicles relative to the roadway and lane markings, this is used in generating the vehicle’s path polynomials, [0054] Vehicle lane with is computed based upon roadway markings, [0057] Vehicle paths polynomials are determined based upon the vehicle’s paths and the roadway lanes. Examiner is interpreting the calculation of vehicle polynomials as the ratio utilizing the lane widths, and distances between vehicles on the roadway because this calculation uses a B spline computation which is defined by a ratio of coefficients, in this case, the data used is for the vehicle positions and lane markings);
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(Sithiravel, [0043], emphasis added)
and determining, the second distance relationship value as the second distance ratio (Sithiravel, [0043] lateral and longitudinal coordinates of the vehicle are calculated to obtain the lateral and longitudinal distances of the vehicles relative to the roadway and lane markings, this is used in generating the vehicle’s path polynomials, [0057] Vehicle paths polynomials are determined based upon the vehicle’s paths and the roadway lanes. Examiner is interpreting the vehicle polynomials as the distance relationship utilizing the land widths, and distances between vehicles on the roadway).
Claims 2, 11 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sithiravel (US 20210089791 A1), in view of Ferguson (US B8504233 B1), and in further view of Schmudderich (EP 2942765 B1) and Goto (US20230166763 A1).
Regarding claim 2 The combination of Sithiravel, Ferguson and Schmudderich discloses; The method according to claim 1, wherein the obtaining, based on the laser point cloud data, a first distance relationship value between a center line of a lane in which the current vehicle is located and the target vehicle comprises (Sithiravel, [0029] Sensors may collect data related to the vehicle… sensors can detect phenomena such as … locations of neighboring vehicles [0053] Vehicle sensor data can be a lidar point cloud, [0057] Vehicles relationship with respect to the center of the roadway lane can be calculated using sensor data):
[obtaining, based on a high-definition map, a center line point set of the lane in which the current vehicle is located, wherein the center line point set comprises coordinates of a plurality of sampling points on the center line of the lane in which the current vehicle is located in a world coordinate system;]
and obtaining, based on the laser point cloud data and the center line point set, the first distance relationship value between the center line of the lane in which the current vehicle is located and the target vehicle (Sithiravel, ([0029] Sensors may collect data related to the vehicle… sensors can detect phenomena such as … locations of neighboring vehicles [0053] Vehicle sensor data can be a lidar point cloud [0057] Vehicles relationship with respect to the center of the roadway lane can be calculated using sensor data).
The combination does not teach; obtaining, based on a high-definition map, a center line point set of the lane in which the current vehicle is located, wherein the center line point set comprises coordinates of a plurality of sampling points on the center line of the lane in which the current vehicle is located in a world coordinate system;
However, in the same field of endeavor Goto teaches; obtaining, based on a high-definition map (Goto, [0027] The high definition map includes lane information), a center line point set of the lane in which the current vehicle is located (Goto, [0027] the High def map contains information of the lane nodes that indicate reference points of the lane lines [0028] the lane information is in the form of identification numbers and coordinates), wherein the center line point set comprises coordinates of a plurality of sampling points on the center line of the lane in which the current vehicle is located in a world coordinate system (Goto, [0028] and [0029] the lane information is coordinates that correspond to the 3D position information in the world, the multiple coordinates are being interpreted as multiple points);
The combination of Sithiravel, Ferguson, Schmudderich and Goto would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the addition of Goto lies that the use of a high-definition map can show more precise location data than a lower definition map. (Goto [0027] and [0028])
Regarding claim 11 the combination of Sithiravel, Ferguson, Schmudderich and Goto teaches; The apparatus according to claim 10, wherein the programming instructions instruct the at least one processor to perform the following operations:
obtaining, based on a high-definition map, (Goto, [0027] The high definition map includes lane information), a center line point set of the lane in which the current vehicle is located (Goto, [0027] the High def map contains information of the lane nodes that indicate reference points of the lane lines [0028] the lane information is in the form of identification numbers and coordinates), wherein the center line point set comprises coordinates of a plurality of sampling points on the center line of the lane in which the current vehicle is located in a world coordinate system (Goto, [0028] and [0029] the lane information is coordinates that correspond to the 3D position information in the world, the multiple coordinates are being interpreted as multiple points);
and obtaining, based on the laser point cloud data and the center line point set, the first distance relationship value between the center line of the lane in which the current vehicle is located and the target vehicle (Sithiravel, ([0029] Sensors may collect data related to the vehicle… sensors can detect phenomena such as … locations of neighboring vehicles [0053] Vehicle sensor data can be a lidar point cloud [0057] Vehicles relationship with respect to the center of the roadway lane can be calculated using sensor data).
The combination of Sithiravel, Ferguson, Schmudderich and Goto would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the addition of Goto lies that the use of a high-definition map can show more precise location data than a lower definition map. (Goto [0027] and [0028])
Regarding claim 20 the combination of Sithiravel, Ferguson, Schmudderich and Goto teaches; The non-transitory computer-readable storage medium according to claim 19, wherein the obtaining, based on the laser point cloud data, a first distance relationship value between a center line of a lane in which the current vehicle is located and the target vehicle comprises (Sithiravel, [0029] Sensors may collect data related to the vehicle… sensors can detect phenomena such as … locations of neighboring vehicles [0053] Vehicle sensor data can be a lidar point cloud, [0057] Vehicles relationship with respect to the center of the roadway lane can be calculated using sensor data):
obtaining, based on a high-definition map, (Goto, [0027] The high definition map includes lane information), a center line point set of the lane in which the current vehicle is located (Goto, [0027] the High def map contains information of the lane nodes that indicate reference points of the lane lines [0028] the lane information is in the form of identification numbers and coordinates), wherein the center line point set comprises coordinates of a plurality of sampling points on the center line of the lane in which the current vehicle is located in a world coordinate system (Goto, [0028] and [0029] the lane information is coordinates that correspond to the 3D position information in the world, the multiple coordinates are being interpreted as multiple points);
and obtaining, based on the laser point cloud data and the center line point set, the first distance relationship value between the center line of the lane in which the current vehicle is located and the target vehicle(Sithiravel, ([0029] Sensors may collect data related to the vehicle… sensors can detect phenomena such as … locations of neighboring vehicles [0053] Vehicle sensor data can be a lidar point cloud [0057] Vehicles relationship with respect to the center of the roadway lane can be calculated using sensor data).
The combination of Sithiravel, Ferguson, Schmudderich and Goto would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the addition of Goto lies that the use of a high-definition map can show more precise location data than a lower definition map. (Goto [0027] and [0028])
Claims 3, 12, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Sithiravel (US 20210089791 A1), in view of Ferguson (US B8504233 B1) and Schmudderich (EP 2942765 B1), in further view of Goto (US20230166763 A1) and Chen (CN 111145574 A).
Regarding claim 3 the combination of Sithiravel, Ferguson, Schmudderich and Goto teaches; The method according to claim 2, wherein the obtaining, based on the laser point cloud data and the center line point set, the first distance relationship value between the center line of the lane in which the current vehicle is located and the target vehicle comprises (Sithiravel, ([0029] Sensors may collect data related to the vehicle… sensors can detect phenomena such as … locations of neighboring vehicles [0053] Vehicle sensor data can be a lidar point cloud [0057] Vehicles relationship with respect to the center of the roadway lane can be calculated using sensor data):
obtaining, based on the laser point cloud data, first coordinates of the target vehicle in a self-vehicle coordinate system of the current vehicle (Sithiravel, [0028] geographical coordinates of the vehicle maybe provided (self-vehicle coordinate) and distances between the vehicle and a target vehicle may be provided, [0043] vehicle coordinate system graph may have measure locations of vehicles (target vehicle));
converting the first coordinates into second coordinates of the target vehicle in the world coordinate system (Sithiravel, [0043] and [0044] Vehicle coordinates for the target vehicle are computed, the vehicle polynomial path is measure to get measured locations of the vehicle (negative coordinates) and then this data is used to predict the coordinates of the vehicle’s next location (positive coordinates) in relation to the location of the self-driving vehicle. Examiner is interpreting this as a conversion of coordinates because the coordinates must be determined around the position of the self-vehicle, therefore the positions must be converted to coordinates based upon the self-vehicle’s location);
determining a first distance between the center line of the lane in which the current vehicle is located and the target vehicle as a minimum distance between the coordinates of the sampling points comprised in the center line point set in the world coordinate system and the second coordinates (Sithiravel, [0025] vehicle is controlled such a distance between vehicles (minimum distance) is achieved [0043] vehicle locations are mapped on a coordinate graph, the vehicle distances are measured using a vehicle path polynomial [0053] Vehicle sensor data can be a lidar point cloud [0057] Vehicles relationship with respect to the center of the roadway lane can be calculated using sensor data);
obtaining a width of the lane in which the current vehicle is located (Sithiravel, [0054] Lane width is used in computed the vehicle path polynomials; therefore, the vehicle lane widths must be computed);
[calculating a first ratio of the first distance to the width of the lane in which the current vehicle is located;]
and determining, as the first ratio, the first distance relationship value between the center line of the lane in which the current vehicle is located and the target vehicle (Sithiravel, [0057] Vehicle’s relationships to the center of the lane are determined via vehicle polynomials to determine if vehicles are deviating from the center of the lane, Examiner is interpreting the relationship between the vehicle, target vehicle and center of the lane as the ratio).
The combination of Sithiravel, Ferguson, Schmudderich and Goto does not teach;
calculating a first ratio of the first distance to the width of the lane in which the current vehicle is located;
However, in the same field of endeavor, Chen teaches;
calculating a first ratio of the first distance to the width of the lane in which the current vehicle is located (Chen, claim 6, Calculating the distance magnitude relationship between the lane width and the lane position of the self-vehicle and the far vehicle);
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(Chen, emphasis added)
The combination of Sithiravel, Ferguson, Schmudderich, Goto and Chen would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that determining positional information between the lane widths, the self-vehicle and target vehicles provides more accurate data for the prevention of collisions. (Chen [0066])
Regarding claim 12 The combination of Sithiravel, Ferguson, Schmudderich, Goto and Chen teach; The apparatus according to claim 11, wherein the one or more memories store the programming instructions for execution by the at least one processor to perform the following operations (Sithiravel, [0025] instructions are stored in a memory and executed by processors):
obtaining, based on the laser point cloud data, first coordinates of the target vehicle in a self-vehicle coordinate system of the current vehicle (Sithiravel, [0028] geographical coordinates of the vehicle maybe provided (self-vehicle coordinate) and distances between the vehicle and a target vehicle may be provided, [0043] vehicle coordinate system graph may have measure locations of vehicles (target vehicle));
converting the first coordinates into second coordinates of the target vehicle in the world coordinate system (Sithiravel, [0043] and [0044] Vehicle coordinates for the target vehicle are computed, the vehicle polynomial path is measure to get measured locations of the vehicle (negative coordinates) and then this data is used to predict the coordinates of the vehicle’s next location (positive coordinates) in relation to the location of the self-driving vehicle. Examiner is interpreting this as a conversion of coordinates because the coordinates must be determined around the position of the self-vehicle, therefore the positions must be converted to coordinates based upon the self-vehicle’s location);
determining a first distance between the center line of the lane in which the current vehicle is located and the target vehicle as a minimum distance between the coordinates of the sampling points comprised in the center line point set in the world coordinate system and the second coordinates (Sithiravel, [0025] vehicle is controlled such a distance between vehicles (minimum distance) is achieved , [0043] vehicle locations are mapped on a coordinate graph, the vehicle distances are measured using a vehicle path polynomial [0053] Vehicle sensor data can be a lidar point cloud [0057] Vehicles relationship with respect to the center of the roadway lane can be calculated using sensor data);
obtaining a width of the lane in which the current vehicle is located (Sithiravel, [0054] Lane width is used in computed the vehicle path polynomials; therefore, the vehicle lane widths must be computed);
calculating a first ratio of the first distance to the width of the lane [[line]] in which the current vehicle is located (Chen, claim 6, Calculating the distance magnitude relationship between the lane width and the lane position of the self-vehicle and the far vehicle);
and determining, as the first ratio, the first distance relationship value between the center line of the lane in which the current vehicle is located and the target vehicle (Sithiravel, [0057] Vehicle’s relationships to the center of the lane are determined via vehicle polynomials to determine if vehicles are deviating from the center of the lane, Examiner is interpreting the relationship between the vehicle, target vehicle and center of the lane as the ratio).
The combination of Sithiravel, Ferguson, Schmudderich, Goto and Chen would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that determining positional information between the lane widths, the self-vehicle and target vehicles provides more accurate data for the prevention of collisions. (Chen [0066])
Regarding claim 21 The combination of Sithiravel, Ferguson, Schmudderich, Goto and Chen teaches; The non-transitory computer-readable storage medium according to claim 20, wherein the obtaining, based on the laser point cloud data and the center line point set, the first distance relationship value between the center line of the lane in which the current vehicle is located and the target vehicle comprises (Sithiravel, ([0029] Sensors may collect data related to the vehicle… sensors can detect phenomena such as … locations of neighboring vehicles [0053] Vehicle sensor data can be a lidar point cloud [0057] Vehicles relationship with respect to the center of the roadway lane can be calculated using sensor data):
obtaining, based on the laser point cloud data, first coordinates of the target vehicle in a self-vehicle coordinate system of the current vehicle (Sithiravel, [0028] geographical coordinates of the vehicle maybe provided (self-vehicle coordinate) and distances between the vehicle and a target vehicle may be provided, [0043] vehicle coordinate system graph may have measure locations of vehicles (target vehicle));
converting the first coordinates into second coordinates of the target vehicle in the world coordinate system (Sithiravel, [0043] and [0044] Vehicle coordinates for the target vehicle are computed, the vehicle polynomial path is measure to get measured locations of the vehicle (negative coordinates) and then this data is used to predict the coordinates of the vehicle’s next location (positive coordinates) in relation to the location of the self-driving vehicle. Examiner is interpreting this as a conversion of coordinates because the coordinates must be determined around the position of the self-vehicle, therefore the positions must be converted to coordinates based upon the self-vehicle’s location);
determining a first distance between the center line of the lane in which the current vehicle is located and the target vehicle as a minimum distance between the coordinates of the sampling points comprised in the center line point set in the world coordinate system and the second coordinates (Sithiravel, [0025] vehicle is controlled such a distance between vehicles (minimum distance) is achieved , [0043] vehicle locations are mapped on a coordinate graph, the vehicle distances are measured using a vehicle path polynomial [0053] Vehicle sensor data can be a lidar point cloud [0057] Vehicles relationship with respect to the center of the roadway lane can be calculated using sensor data);
obtaining a width of the lane in which the current vehicle is located (Sithiravel, [0054] Lane width is used in computed the vehicle path polynomials; therefore, the vehicle lane widths must be computed);
calculating a first ratio of the first distance to the width of the lane [[line]] in which the current vehicle is located (Chen, claim 6, Calculating the distance magnitude relationship between the lane width and the lane position of the self-vehicle and the far vehicle);
and determining, as the first ratio, the first distance relationship value between the center line of the lane in which the current vehicle is located and the target vehicle (Sithiravel, [0057] Vehicle’s relationships to the center of the lane are determined via vehicle polynomials to determine if vehicles are deviating from the center of the lane, Examiner is interpreting the relationship between the vehicle, target vehicle and center of the lane as the ratio).
The combination of Sithiravel, Ferguson, Schmudderich, Goto and Chen would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that determining positional information between the lane widths, the self-vehicle and target vehicles provides more accurate data for the prevention of collisions. (Chen [0066])
Allowable Subject Matter
Claims 7, 16 and 23 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Neither the closest known prior art or any reasonable combination thereof teaches;
obtaining a reciprocal of a smaller value between the first fitting degree and the second fitting degree;
and determining, as the reciprocal;
and the obtaining the second confidence of the plurality of
obtaining a reciprocal of a smaller value between the third fitting degree and the fourth fitting degree;
and determiningas the reciprocal.
Further, claims 8-9, 17-18 and 24 are dependent on claims 7, 16 and 23 respectively, and would therefore also be allowable if rewritten in independent form.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
[1] Horinaga, US-11308337 B2, teaches a method of mapping vehicle distances and paths to determine if changing lanes is safe.
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/J.M.E./Examiner, Art Unit 2666 /EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666