DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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.
Claims 11-13, 15-17, 21-33 are rejected under 35 U.S.C. 103 as being unpatentable over Tam (US 2023/0012853) in view of Orenstein (US 2022/0135029), and Kinoshita (US 2022/0161787)
As to claim 11 Tam discloses a method for controlling an autonomous vehicle, the method comprising:
determining, by one or more processors, an uncertainty value for an object detected by the autonomous vehicle, the object being external to the autonomous vehicle(Paragraph 126 “Referring again to FIG. 6, it can be determined whether the vehicle 602 can pass the hazard zone given a distance between the right and left hard boundaries of the bins 616, 618. The distance should be larger than a threshold distance for the vehicle 602 to pass, where the threshold distance can be related to a width of the vehicle 602 (e.g., at least 1.2, 1.5, etc., time the width of the vehicle 602). In an example, a distance 620 between the boundary 614 of the hazard zone (e.g., target lateral constraint) and the left lane boundary 608 may be too narrow for the vehicle 602 to drive (i.e., fit) through. If the left lane boundary 608 were the limit of the drivable area (e.g., because it is a hard boundary—physically, legally, or otherwise), a location 622 corresponding to the bin 616 is a static blockage. That is, the vehicle 602 cannot pass the object(s) represented by the boundary points of the bins 616, 618. Accordingly, the object avoidance layer 406 can adjust the discrete-time speed plan to stop the vehicle before or upon reaching the location 622. ”),
determining, by the one or more processors, a lateral gap threshold for the object based on the uncertainty value and a baseline lateral gap threshold(Paragraph 126 “Referring again to FIG. 6, it can be determined whether the vehicle 602 can pass the hazard zone given a distance between the right and left hard boundaries of the bins 616, 618. The distance should be larger than a threshold distance for the vehicle 602 to pass, where the threshold distance can be related to a width of the vehicle 602 (e.g., at least 1.2, 1.5, etc., time the width of the vehicle 602). In an example, a distance 620 between the boundary 614 of the hazard zone (e.g., target lateral constraint) and the left lane boundary 608 may be too narrow for the vehicle 602 to drive (i.e., fit) through. If the left lane boundary 608 were the limit of the drivable area (e.g., because it is a hard boundary—physically, legally, or otherwise), a location 622 corresponding to the bin 616 is a static blockage. That is, the vehicle 602 cannot pass the object(s) represented by the boundary points of the bins 616, 618. Accordingly, the object avoidance layer 406 can adjust the discrete-time speed plan to stop the vehicle before or upon reaching the location 622. ”); and
controlling, by the one or more processors, the autonomous vehicle in an autonomous driving mode based on the lateral gap threshold for the object(Paragraph 111-112 “Although described herein at times with reference to an autonomous vehicle, the methods and apparatus described herein may be implemented in any vehicle capable of autonomous or semi-autonomous operation, such as one including an ADAS. Although described with reference to a vehicle transportation network, the method and apparatus described herein may include the vehicle operating in any area navigable by the vehicle. In brief, the method 500 for proactively mitigating risk to a vehicle traversing a vehicle transportation network can include determining respective hazard zones for detected hazard objects, which hazard zones respectively define a target lateral constraint associated with the hazard object, e.g., based on its state as a static hazard object (also referred to as a static object) or as a dynamic hazard object (also referred to as a dynamic object). A target lateral constraint may be one that allows the vehicle to avoid the hazard object without a speed constraint (e.g., without modifying the current time speed plan). Lateral buffer algorithms may be used where hazard zones are present in the same discretized time and location (e.g., they overlap in the longitudinal direction) to determine a final size of lateral buffers. The allocation may be determined based on a cost function that optimizes for risk posed by the hazard objects, including the target lateral constraints, as described in more detail below”)..
Tam does not explicitly disclose determining of the uncertainty value being based at least in part on cross-track error defined for a current location of the autonomous vehicle;
Orenstein teaches determining of the uncertainty value being based at least in part on cross-track error defined for a current location of the autonomous vehicle (Paragraph 50-52 “In some examples, the model component 104 may determine the probability for an object path to intersect with a trajectory of the vehicle 102 based at least in part on one or more uncertainty values associated with one or more of: an object position, an object acceleration, an object velocity, an acceleration of the vehicle, etc. For instance, uncertainty values may be determined to account for deviations in position, velocity, and/or acceleration that the object and/or the vehicle may follow at a future time. In at least some examples, such uncertainties may be associated with errors in the models for perception and/or prediction.”).
It would have been obvious to one of ordinary skill to modify Tam to include the teachings of determining an error value for a boundary of an object for the purpose of avoiding collision between the vehicle and the object.
Tam does not explicitly disclose a baseline lateral gap threshold, wherein the lateral gap threshold defines a margin of safety of driving next to the object.
Kinoshita teaches a baseline lateral gap threshold, wherein the lateral gap threshold defines a margin of safety of driving next to the object (Paragraph 139 “First, a unit gap Gs[i] related to the object 5[i] will be described. The unit gap Gs[i] is a lateral distance between the vehicle 1 and the object 5[i] and is a lateral distance at which the occupant does not feel uneasy when the vehicle 1 passes by the object 5[i]. That is, the unit gap Gs[i] is a target lateral distance. The unit gap Gs[i] is determined in advance for each object 5[i]. The unit gap Gs[i] may be a predetermined value different for each type of the object 5. For example, the unit gap Gs (e.g., 3 m) in the case where the object 5 is a pedestrian is larger than the unit gap Gs (e.g., 2 m) in the case where the object 5 is a parked vehicle. The unit gap Gs[i] may be set based on the distribution parameter σy (see FIG. 5) of the obstacle potential field Uo[i]. The information of the unit gap Gs[i] is included in the above-mentioned potential function information 300, for example.”)
It would have been obvious to one of ordinary skill to modify Tam to include the teachings of a baseline lateral gap threshold for the purpose of avoiding collision between the vehicle and the object.
As to claim 12 Kamenev teaches a method wherein the cross-track error is a mean value or a standard deviation value (Paragraph 77).
As to claim 13 Tam discloses a method further comprising determining the baseline lateral gap threshold based on current driving conditions (Paragraph 185).
As to claim 15 Tam discloses a method wherein the current driving conditions include whether the autonomous vehicle is driving in an urban area (Paragraph 107, 192).
As to claim 16 Tam discloses a method wherein the current driving conditions include whether the autonomous vehicle is driving in a suburban area(Paragraph 107, 192).
As to claim 17 discloses a method further comprising determining the baseline lateral gap threshold based on a type of the object(Paragraph 134)..
As to claim 21 Tam discloses a method wherein determining the lateral gap threshold for the object further includes identifying an interaction threshold corresponding to a predetermined percentage of an expected interaction rate with objects (Paragraph 39).
As to claim 22 Tam discloses a method wherein identifying the interaction threshold is based on a type of the object (Paragraph 134).
As to claim 23 Tam discloses a method wherein controlling the autonomous vehicle further includes:
attempting to solve for a trajectory that meets the lateral gap threshold for the object (Abstract); and
when a trajectory that meets the lateral gap threshold for the object cannot be solved for, adjusting the baseline lateral gap threshold to determine an adjusted lateral gap threshold until a trajectory that meets the adjusted lateral gap threshold for the object is found (Paragraph 8).
As to claim 24 the claim is interpreted and rejected as in claim 11.
As to claim 25 the claim is interpreted and rejected as in claim 12.
As to claim 26 the claim is interpreted and rejected as in claim 13.
As to claim 28 the claim is interpreted and rejected as in claim 17.
As to claim 29 the claim is interpreted and rejected as in claim 21.
As to claim 30 the claim is interpreted and rejected as in claim 22.
As to claim 31 the claim is interpreted and rejected as in claim 23.
As to claim 32 the claim is interpreted and rejected as in claim 11.
As to claim 33 the claim is interpreted and rejected as in claim 11.
Claims 14, 27 are rejected under 35 U.S.C. 103 as being unpatentable over Tam (US 2023/0012853) in view of Orenstein (US 2022/0135029), and Kinoshita (US 2022/0161787), as applied to claim 4 above, and in further view of Bagschik (US 2021/0094540)
As to claim 14 Bagschik teaches a method wherein the current driving conditions include whether it is currently precipitating in a driving environment of the autonomous vehicle (Paragraph 15).
It would have been obvious to one of ordinary skill to modify Tam to include the teachings of determining the driving conditions of the environment for the purpose of safely navigating the vehicle along the route.
As to claim 27 the claim is interpreted and rejected as in claim 14-16.
Claims 34-35 are rejected under 35 U.S.C. 103 as being unpatentable over Tam (US 2023/0012853) in view of Orenstein (US 2022/0135029), and Kinoshita (US 2022/0161787) as applied to claim 11 above, and in further view of Belman (US 2023/0286536)
As to claim 34 Belman teaches a method further comprising determining the baseline lateral gap threshold based on whether the autonomous vehicle is currently driving in either an urban area or a suburban area, wherein a baseline lateral gap threshold is based on the autonomous vehicle currently driving in the urban area is shorter than a lateral gap threshold that is based on the autonomous vehicle currently driving in the suburban area (Paragraph 890). It would have been obvious to one of ordinary skill to modify Orenstein to include the teachings of setting the separation distance based on the location of the vehicle for the purpose of maintaining a safe separation between vehicles.
As to claim 35 discloses a method wherein the baseline lateral gap threshold is based on the driving environment including precipitation is larger than the baseline lateral gap threshold that is based on the driving environment not including the precipitation (Paragraph 871).
Claim 36 is rejected under 35 U.S.C. 103 as being unpatentable over Tam (US 2023/0012853) in view of Orenstein (US 2022/0135029), and Kinoshita (US 2022/0161787) as applied to claim 11 above, and in further view of Ollis (US 2022/0340137)
As to claim 36 Ollis teaches a method further comprising determining the baseline lateral gap threshold based on a current driving situation of the autonomous vehicle using at least one of heuristics or machine learning in order to control the autonomous vehicle in the autonomous driving mode according to socially acceptable human driving behaviors associated with the current driving situation(Paragraph 56). It would have been obvious to one of ordinary skill to modify Tam to include the teachings of the lateral offset being based on socially acceptable distance for the purpose of creating an environment that is safe for the particular area.
Response to Arguments
Applicant’s arguments with respect to claims 11-17, 21-36 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
On page 7 of the applicants arguments applicants argue that Tam in view of Orenstein does not each “the determining of the uncertainty value being based at least in part on cross-track error defined for a current location of the autonomous vehicle”.
The examiner respectfully disagrees with the applicants arguments. The applicant is reminded that the examiner interprets the claim with the broadest reasonable interpretation. Orenstein teaches determining of the uncertainty value being based at least in part on cross-track error defined for a current location of the autonomous vehicle (Paragraph 50-52 “In some examples, the model component 104 may determine the probability for an object path to intersect with a trajectory of the vehicle 102 based at least in part on one or more uncertainty values associated with one or more of: an object position, an object acceleration, an object velocity, an acceleration of the vehicle, etc. For instance, uncertainty values may be determined to account for deviations in position, velocity, and/or acceleration that the object and/or the vehicle may follow at a future time. In at least some examples, such uncertainties may be associated with errors in the models for perception and/or prediction.”). Orenstein clearly teaches of uncertainty values that account for deviations in position velocity and/or acceleration[cross track error] that the vehicle and/or the object may follow at a future time. The cross track error is the deviation of the vehicles current path to an intended path and in this case the system measures the uncertainty values based on deviation in the position that the vehicle may follow at a future time.
On page 8 of the applicants arguments applicants argue that the prior art does not disclose “determining the baseline lateral gap threshold based on current driving conditions”.
The examiner respectfully disagrees with the applicants arguments. Tam discloses a method further comprising determining the baseline lateral gap threshold based on current driving conditions (Paragraph 185 “The hazard zone determined at 506 and the associated lateral and longitudinal constraints is associated with a trajectory of the predicted range of trajectories……In some implementations, a trajectory from the range of trajectories is selected based on, for example, the presence of other hazard objects, the conditions in which the dynamic hazard object is traveling (e.g., weather, type of road, etc.), or other conditions that may make one trajectory more likely than another.”). Here Tam selects the trajectory based on the conditions of the environment which can include the weather, type of road, etc. In step 516 in Figure 5 the system determines a lateral buffer to respective lateral constraints for the hazard zones which allows for maintaining a minimum passing gap(Paragraph 193).
On page 9 of the applicants arguments applicants argue that the prior art does not disclose “wherein the current driving conditions include whether the autonomous vehicle is driving in an urban area”
The examiner respectfully disagrees with the applicants arguments. Tam teaches wherein the current driving conditions include whether the autonomous vehicle is driving in an urban area”(Paragraphs192 “For example, narrow streets (e.g., residential streets), streets with multiple hazard objects arranged in parallel on one or both sides of the vehicle that narrow an otherwise wide street (e.g., city streets), etc. To address these and other situations with overlapping hazard zones, dual-sided buffer allocation may be used.”)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IMRAN K MUSTAFA whose telephone number is (571)270-1471. The examiner can normally be reached Mon-Fri 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James J Lee can be reached at 571-270-5965. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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IMRAN K. MUSTAFA
Primary Examiner
Art Unit 3668
/IMRAN K MUSTAFA/ Primary Examiner, Art Unit 3668
9/18/2026
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