DETAILED ACTION
Claims 1-20 are considered in this office action. Claims 1-20 are pending examination.
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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-10, 12-17 and 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Pendleton (US12269473B2) and herein after will be referred as Pendleton.
Regarding Claim 1, Pendleton teaches a vehicle control device of a vehicle (Fig.4 #406 Col.11 Line 23-31: “FIG. 4 shows an example architecture 400 for an autonomous vehicle (e.g., the AV 100 shown in FIG. 1). The architecture 400 includes a perception module 402 (sometimes referred to as a perception circuit), a planning module 404 (sometimes referred to as a planning circuit), a control module 406 (sometimes referred to as a control circuit), a localization module 408 (sometimes referred to as a localization circuit), and a database module 410 (sometimes referred to as a database circuit).”), the vehicle control device comprising:
memory configured to store map information (Col.7 Line 3-13: “In an embodiment, memory 144 is similar to the main memory 306 described below. In an embodiment, the data storage unit 142 and memory 144 store historical, real-time, and/or predictive information about the environment 190. In an embodiment, the stored information includes maps, driving performance, traffic congestion updates or weather conditions. In an embodiment, data relating to the environment 190 is transmitted to the AV 100 via a communications channel from a remotely located database 134.”);
a sensor configured to determine at least a location of the vehicle or a heading of the vehicle (Col.6 Line 52-62: “In an embodiment, the AV system 120 includes sensors 121 for measuring or inferring properties of state or condition of the AV 100, such as the AV's position, linear and angular velocity and acceleration, and heading (e.g., an orientation of the leading end of AV 100). Example of sensors 121 are GPS, inertial measurement units (IMU) that measure both vehicle linear accelerations and angular rates, wheel speed sensors for measuring or estimating wheel slip ratios, wheel brake pressure or braking torque sensors, engine torque or wheel torque sensors, and steering angle and angular rate sensors.” and also See Col.11 Line 23-26)
and a processor configured to: determine, based on at least one of the location of the vehicle or the heading of the vehicle, an intersection on a path of the vehicle; determine, based on the map information, a region of interest that comprises the intersection; determine, based on at least one of a traffic lane in the region of interest or a lane attribute of the traffic lane, an intersection attribute of the intersection (Col.11 Line 56- Col.12 Line 6 : “The planning module 404 also receives data representing the AV position 418 from the localization module 408. The localization module 408 determines the AV position by using data from the sensors 121 and data from the database module 410 (e.g., a geographic data) to calculate a position. For example, the localization module 408 uses data from a GNSS (Global Navigation Satellite System) sensor and geographic data to calculate a longitude and latitude of the AV. In an embodiment, data used by the localization module 408 includes high-precision maps of the roadway geometric properties, maps describing road network connectivity properties, maps describing roadway physical properties (such as traffic speed, traffic volume, the number of vehicular and cyclist traffic lanes, lane width, lane traffic directions, or lane marker types and locations, or combinations of them), and maps describing the spatial locations of road features such as crosswalks, traffic signs or other travel signals of various types.”);
and control, based on the intersection attribute, an operation of the vehicle (Col.12 L7-17 : “The control module 406 receives the data representing the trajectory 414 and the data representing the AV position 418 and operates the control functions 420 a-c (e.g., steering, throttling, braking, ignition) of the AV in a manner that will cause the AV 100 to travel the trajectory 414 to the destination 412. For example, if the trajectory 414 includes a left turn, the control module 406 will operate the control functions 420 a-c in a manner such that the steering angle of the steering function will cause the AV 100 to turn left and the throttling and braking will cause the AV 100 to pause and wait for passing pedestrians or vehicles before the turn is made.” Also See Figures 13A-L displaying multiple scenarios for controlling the host vehicle at Fourways stop sign.).
Similarly Claim 13 is rejected on the similar rational.
Regarding Claim 2, Pendleton teaches the vehicle control device of claim 1.
Pendleton also teaches wherein the processor is further configured to: determine, from the map information and based on the location of the vehicle, a lane segment that diverges or merges within the path of the vehicle; and determine, within the region of interest, the intersection that comprises the lane segment (Col.11 Line 60-Col.12 Line 6: “The planning module 404 also receives data representing the AV position 418 from the localization module 408. The localization module 408 determines the AV position by using data from the sensors 121 and data from the database module 410 (e.g., a geographic data) to calculate a position. For example, the localization module 408 uses data from a GNSS (Global Navigation Satellite System) sensor and geographic data to calculate a longitude and latitude of the AV. In an embodiment, data used by the localization module 408 includes high-precision maps of the roadway geometric properties, maps describing road network connectivity properties, maps describing roadway physical properties (such as traffic speed, traffic volume, the number of vehicular and cyclist traffic lanes, lane width, lane traffic directions, or lane marker types and locations, or combinations of them), and maps describing the spatial locations of road features such as crosswalks, traffic signs or other travel signals of various types”).
Similarly Claim 14 is rejected on the similar rational.
Regarding Claim 3, Pendleton teaches the vehicle control device of claim 1.
Pendleton also teaches wherein the processor is further configured to: determine, based on at least one of a starting point of a lane segment or an end point of the lane segment, the lane attribute, and wherein the lane segment comprises a portion, of the traffic lane in which the vehicle is traveling, that is inside the region of interest (Figures 13J-L).
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Similarly Claim 15 is rejected on the similar rational.
Regarding Claim 4, Pendleton teaches the vehicle control device of claim 3.
Pendleton also teaches wherein the processor is further configured to: determine a first vector tangent to the lane segment at the starting point of the lane segment; determine a second vector tangent to the lane segment at the end point of the lane segment; and determine, based on an angle between the first vector and the second vector, whether the path of the vehicle along the lane segment is straight (Fig.13 E #1310).
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Similarly Claim 16 is rejected on the similar rational.
Regarding Claim 5, Pendleton teaches the vehicle control device of claim 4.
Pendleton also teaches wherein the processor is further configured to: determine, based on a cross product of the first vector and the second vector, whether the path of the vehicle comprises a left turn or a right turn (Fig. 13I).
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Similarly Claim 17 is rejected on the similar rational.
Regarding Claim 7, Pendleton teaches the vehicle control device of claim 1.
Pendleton also teaches wherein the processor is further configured to: determine drivable lanes within the intersection; and determine directed graphs respectively corresponding to the drivable lanes (Fig.10).
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Similarly Claim 19 is rejected on the similar rational.
Regarding Claim 8, Pendleton teaches the vehicle control device of claim 7.
Pendleton also teaches wherein the processor is further configured to: determine, based on the directed graphs, a type of the intersection (Col.17 Line 24-38; “As shown in FIG. 13A, AV 100 is stopped at a primary stopline 1308 of the intersection. The primary stopline 1308 is a real or virtual line where a vehicle is expected to stop at the intersection. For example, the primary stopline 1308 corresponds to the expected stopping position as designated by stop sign 1304 and stop road marking 1306. Based on destination of AV 100, AV system 120 determines a planned travel path 1310 of AV 100 through the intersection (e.g., a path the AV 100 is expected to take from the stopline 1308 to an exit of the intersection based on the destination of AV 100). The AV system 120 can also determine a travel lane 1312 corresponding to (e.g., overlapping, nearest to) the planned travel path 1310. In some embodiments, the travel lane 1312 corresponds to one or more lanes of the roadway where the AV 100 is planning to go.”).
Similarly Claim 20 is rejected on the similar rational.
Regarding Claim 9, Pendleton teaches the vehicle control device of claim 8.
Pendleton also teaches wherein the type of the intersection comprises at least one of: a three-way intersection, a three-way intersection without a left turn, a four-way intersection, a five-way intersection, a roundabout, an overpass, or an underpass (Col.11 Line 56-Col.2 Line 6: “The planning module 404 also receives data representing the AV position 418 from the localization module 408. The localization module 408 determines the AV position by using data from the sensors 121 and data from the database module 410 (e.g., a geographic data) to calculate a position. For example, the localization module 408 uses data from a GNSS (Global Navigation Satellite System) sensor and geographic data to calculate a longitude and latitude of the AV. In an embodiment, data used by the localization module 408 includes high-precision maps of the roadway geometric properties, maps describing road network connectivity properties, maps describing roadway physical properties (such as traffic speed, traffic volume, the number of vehicular and cyclist traffic lanes, lane width, lane traffic directions, or lane marker types and locations, or combinations of them), and maps describing the spatial locations of road features such as crosswalks, traffic signs or other travel signals of various types.”).
Regarding Claim 10, Pendleton teaches the vehicle control device of claim 8.
Pendleton also teaches wherein the processor is further configured to determine the intersection attribute by: determining the intersection attribute based on the lane attribute and the type of the intersection (Col.11 Line 56-Col.2 Line 6: “The planning module 404 also receives data representing the AV position 418 from the localization module 408. The localization module 408 determines the AV position by using data from the sensors 121 and data from the database module 410 (e.g., a geographic data) to calculate a position. For example, the localization module 408 uses data from a GNSS (Global Navigation Satellite System) sensor and geographic data to calculate a longitude and latitude of the AV. In an embodiment, data used by the localization module 408 includes high-precision maps of the roadway geometric properties, maps describing road network connectivity properties, maps describing roadway physical properties (such as traffic speed, traffic volume, the number of vehicular and cyclist traffic lanes, lane width, lane traffic directions, or lane marker types and locations, or combinations of them), and maps describing the spatial locations of road features such as crosswalks, traffic signs or other travel signals of various types.”)..
Regarding Claim 12, Pendleton teaches the vehicle control device of claim 1.
Pendleton also teaches wherein the sensor comprises at least one of: a global positioning system (GPS) sensor, a gyroscope, an accelerometer, or a magnetometer (Col.11 Line 56-64).
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 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Pendleton in view of Dorum (US9766081) and herein after will be referred as Dorum.
Regarding Claim 11, Pendleton teaches the vehicle control device of claim 8.
Pendleton does not expressly teach the processor is configured to determine the intersection attribute by: determining, based on the lane attribute successively indicating a plurality of left turns, that the lane attribute further indicates a loop; and determining, based on the lane attribute indicating the loop, that the intersection attribute indicates a roundabout.
Dorum teaches the processor is configured to determine the intersection attribute by: determining, based on the lane attribute successively indicating a plurality of left turns, that the lane attribute further indicates a loop (Col.10 Line 34-43: “At act 2009, the mobile device 122 performs a vector fit for the subset of probe data points for the potential roundabout location. A vector field fit method is performed on the selected vector probe data to intrinsically capture circulation in vector fields. When circulation a vector field is present, the center is a circular singularity point that can be derived from the vector field. Circular singularities in the flow pattern can denote the approximate center of the roundabout. For example, a least squares vector field fit may be used. Other vector field fit methods may be used.”);
and determining, based on the lane attribute indicating the loop, that the intersection attribute indicates a roundabout (Col.10 Line 44-53: “At act 2011, the mobile device 122 determines a roundabout location from the vector fit. The vector fit approximation can be used to determine a circular singularity location and direction of flow (i.e, clockwise or counter clockwise) for the vector fit approximation. The circular singularity location corresponds with the approximate center of a roundabout and the direction of flow corresponds to traffic flow through the roundabout. The optimal center of the roundabout may be determined using a technique such as least median of squares (LMS).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pendleton to incorporate the teachings of Dorum to include the processor is configured to determine the intersection attribute by: determining, based on the lane attribute successively indicating a plurality of left turns, that the lane attribute further indicates a loop; and determining, based on the lane attribute indicating the loop, that the intersection attribute indicates a roundabout. Doing so would optimize the vehicle operation going through a roundabout.
Allowable Subject Matter
Claims 6 and 18 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Oh (US2021/0294341) discloses a method for generating a U-turn path in an autonomous vehicle includes calculating a drivable area, generating multiple paths drivable in the drivable area, filtering a driving strategy path among the multiple paths based on deep learning, and determining a final path from the filtered candidate paths.
Oh (US2021/0004016) discloses a U-turn control system for an autonomous vehicle is provided. The U-turn control system includes a learning device that subdivides information regarding situations to be considered when the autonomous vehicle executes a U-turn for each of a plurality of groups and performs deep learning. A controller executes a U-turn of the autonomous vehicle based on the result learned by the learning device.
Choi et al. (US12117301B2) discloses an apparatus and method for controlling a vehicle. The apparatus may include a sensor that detects a boundary of a road, storage that stores a road map, and a controller. The controller may determine start and end points of a first U-turn path based on a U-turn lane link in the road map, determine an arc path based on the start and end points, determine a second U-turn path centered on a front bumper based on the arc path, determine an offset based on a center point of the turning path and a center point of the road boundary, and determine a third U-turn path by applying the offset to the second U-turn path. Accordingly, stability of autonomous driving may be improved by accurately determining a road boundary in a U-turn area and generating a U-turn driving path in consideration of the turning radius of the vehicle.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDHESH K JHA whose telephone number is (571)272-6218. The examiner can normally be reached M-F:0800-1700.
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/ABDHESH K JHA/Primary Examiner, Art Unit 3668