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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Priority is being given to 03/15/2023.
Status of Claims
This action is in reply to the amendments filed on 03/11/2026.
Claims 1-20 are currently pending and have been examined.
Claims 1-19 are amended.
Claims 1-20 are currently rejected.
This action is made FINAL.
Response to Arguments
Applicant’s arguments filed 03/11/2026 have been fully considered but they are not fully persuasive.
In light of applicant’s amendments to the drawings, the drawing objections have been withdrawn.
Regarding applicant’s arguments that Li and Sahin do not qualify as prior art, they are not persuasive. While both US applications were filed after the effective filing data of this application, Li claims benefit to PCTPCT/CN2021/078239 which gives it an effective filing date of 02/26/2021 and Sahin claims benefit to provisional filing 63/335,648 which gives it an effective filing date of 04/27/2022. For a 102(a)(2) reference to be valid it needs to be effectively filed before the effective filing date of the instant application which has been demonstrated above. A copy of the PCT and provisional have been included to show the subject matter relied upon in the rejection were present in the priority documents. While the provisional of Sahin is not an identical specification, page 5 as provided contains the support for the rejections of Sahin.
In light of the amendments to the claims the 101 rejections have been withdrawn.
Regarding the 112 rejections, in light of the amendments these rejections have been withdrawn.
Applicant’s arguments with regards to the art rejections have been considered and are not persuasive. Applicant argues the Li does not teach the current vehicle is one of the “target vehicles”. However in determining the trajectory of the “target vehicle” in Li, the “interaction feature vector” is between the “target vehicle” and its surrounding vehicles. As can be shown in at least fig. 6, the “current vehicle” if Li is a surrounding vehicle and therefore is included as part of the vectorized data that is gathered to help predict the trajectory of the target vehicle as shown in fig. 6. Applicant additionally argues that “several features in the pending claims are neither taught or suggested”. This amounts to general allegation without specific arguments towards the deficiencies of specific limitations and how the applied art is deficient. Therefore applicant’s arguments are not persuasive and the rejections are maintained.
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 (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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claim(s) 1-4, 9-16, and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Li et. al. (US 2023/0399023), herein Li.
Regarding claim 1:
Li teaches:
A method for predicting a vehicle trajectory (a vehicle driving intention prediction method [0006]), comprising:
obtaining vectorized features of a plurality of target vehicles (to determine an interaction feature vector between the surrounding vehicle and the target vehicle, where the interaction feature vector between the surrounding vehicle and the target vehicle represents impact of the surrounding vehicle on the target vehicle [0010]; a driving feature vector of the target vehicle relative to each of the plurality of lanes [0013]) based on perceived information (The sensor system 104 may include several sensors that can sense information about the ambient environment of the vehicle 100. [0062]) of an autonomous vehicle (vehicle 100 operates in an autonomous mode [0084]), wherein the perceived information is obtained based on data acquired by a sensor of the autonomous vehicle (the sensor system 104 may include a positioning system 122 (the positioning system may be a Global Positioning System (GPS), a BeiDou system, or another positioning system), an inertial measurement unit (IMU) 124, a radar 126, a laser rangefinder 128, and a camera 130. The sensor system 104 may further include sensors (for example, an in-vehicle air quality monitor, a fuel gauge, and an oil temperature gauge) in an internal system of the vehicle 100. Sensor data from one or more of these sensors can be used to detect an object and corresponding features (a location, a shape, a direction, a speed, and the like). Such detection and recognition are key functions of safe operation of the vehicle 100 [0063]);
obtaining a trajectory prediction result (The prediction unit 43 predicts the behavior intention and the future track of the target vehicle [0122]) based on vectorized features of each target vehicle of the plurality of target vehicles (based on current map information and the target information sensed by the sensing unit [0122]), to obtain a plurality of trajectory prediction results of the plurality of target vehicles (The target fusion unit 42, the prediction unit 43, the planning unit 43, and the control unit 45 are all implemented in the processor in FIG. 1 or FIG. 2. In a driving process of the vehicle, an intention of another vehicle is predicted in real time, accurately, and reliably, so that the vehicle can predict a traffic condition in front of the vehicle, and establish a traffic situation around the vehicle. This helps determine importance of the target of another surrounding vehicle of the vehicle, and filter a key target for interaction, so that the vehicle can plan the route in advance and safely pass through a complex road condition scenario. [0122]), wherein the plurality of target vehicles (extracting one or more of a location feature of each of the surrounding vehicles [0149]) comprise the autonomous vehicle (fig. 6, current vehicle) and a plurality of first surrounding vehicles (fig. 6, another vehicle), wherein the plurality of first surrounding vehicles being vehicles in a surrounding environment of the autonomous vehicle that are selected according to a preset first rule (The surrounding vehicle of the target vehicle may be understood as another vehicle that is at a specific distance from the target vehicle. The distance may be set by a user, or may be set by a skilled person, or may be related to a sensing distance of a sensor of the current vehicle. [0145]);
Controlling the autonomous vehicle based on the trajectory prediction result obtained (the application 141 may also be a program for controlling the autonomous vehicle to avoid collision with another vehicle and safely pass through an intersection [0102]).
Regarding claim 2:
Li teaches all the limitations of claim 1, upon which this claim is dependent.
Li further teaches:
wherein obtaining vectorized features of the plurality of target vehicles (a driving feature vector of the target vehicle relative to each of the plurality of lanes [0013]) further comprises:
obtaining, based on the perceived information of the autonomous vehicle (the processor 113 may predict a driving track of another vehicle based on a surrounding road condition and another vehicle condition that are detected by the sensor 153 [0104]), self- vehicle trajectory vectorized features (The planning unit 44 plans a driving route of the vehicle based on a prediction result of the prediction unit and/or output information of the navigation unit 47 [0122]), surrounding trajectory vectorized features (the sensor 153 may detect an animal, a vehicle, an obstacle, or cross walk [0103]), and road network vectorized features of each target vehicle of the plurality of target vehicles (a driving feature implicit vector of the target vehicle relative to each of the plurality of lanes [0010]);
wherein the surrounding trajectory vectorized features are trajectory vectorized features of a plurality of second surrounding vehicles of a target vehicle of the plurality of target vehicles (a driving feature vector of each of the surrounding vehicles relative to the target vehicle [0012]), the plurality of second surrounding vehicles being vehicles in a surrounding environment of the target vehicle that are selected according to a preset second rule (The surrounding vehicle of the target vehicle may be understood as another vehicle that is at a specific distance from the target vehicle. The distance may be set by a user, or may be set by a skilled person, or may be related to a sensing distance of a sensor of the current vehicle. [0145]).
Regarding claim 3:
Li teaches all the limitations of claim 2, upon which this claim is dependent.
Li further teaches:
wherein obtaining the trajectory prediction result (The prediction unit 43 predicts the behavior intention and the future track of the target vehicle [0122]) based on vectorized features of each target vehicle of the plurality of target vehicles (based on current map information and the target information sensed by the sensing unit [0122]) further comprises:
obtaining, for each target vehicle of the plurality of target vehicles, encoded (The target fusion unit 42 processes the environment information around the vehicle sensed by the sensing unit 41, and outputs obstacle target information [0122]) features of the target vehicle (obtaining driving information of the target vehicle [0007]) based on the self-vehicle trajectory vectorized features (The planning unit 44 plans a driving route of the vehicle based on a prediction result of the prediction unit and/or output information of the navigation unit 47 [0122]), the surrounding trajectory vectorized features (the lane intention of the target vehicle based on the driving feature of the surrounding vehicle relative to the target vehicle [0007]), and the road network vectorized features of the target vehicle (the driving feature of the target vehicle relative to each of the plurality of roads [0008]);
and obtaining the trajectory prediction result of the target vehicle based on the encoded features of the target vehicle (The prediction unit 43 predicts the behavior intention and the future track of the target vehicle based on current map information and the target information sensed by the sensing unit. [0122]).
Regarding claim 4:
Li teaches all the limitations of claim 3, upon which this claim is dependent.
Li further teaches:
wherein the obtaining encoded features of the target vehicle based on the self-vehicle trajectory vectorized features (The target fusion unit 42 processes the environment information around the vehicle sensed by the sensing unit 41, and outputs obstacle target information [0122]), the surrounding trajectory vectorized features (the lane intention of the target vehicle based on the driving feature of the surrounding vehicle relative to the target vehicle [0007]), and the road network vectorized features of the target vehicle (the driving feature of the target vehicle relative to each of the plurality of roads [0008]) further comprises:
encoding the self-vehicle trajectory vectorized features, the surrounding trajectory vectorized features, and the road network vectorized features (The target fusion unit 42 processes the environment information around the vehicle sensed by the sensing unit 41, and outputs obstacle target information [0122]) to obtain self-vehicle trajectory encoded features, surrounding trajectory encoded features, and road network encoded features (The target fusion unit 42 processes the environment information around the vehicle sensed by the sensing unit 41, and outputs obstacle target information [0122]), respectively;
performing feature interaction (an interaction feature between the target vehicle and another vehicle, may be determined based on the driving information of the target vehicle and the driving information of the surrounding vehicle of the target vehicle [0148]) on the self-vehicle trajectory encoded features, the surrounding trajectory encoded features, and the road network encoded (The target fusion unit 42 processes the environment information around the vehicle sensed by the sensing unit 41, and outputs obstacle target information [0122]) features to obtain self-vehicle trajectory interaction features (the interaction feature between the target vehicle and the other vehicle [0157]), surrounding trajectory interaction features (the interaction feature between the target vehicle and each road [0157]), and environment interaction features (the interaction feature between the target vehicle and each lane [0157]), respectively ; and
performing feature fusion (The target fusion unit 42 processes the environment information around the vehicle sensed by the sensing unit 41 [0122]) on the self-vehicle trajectory interaction features, the surrounding trajectory interaction features, and the environment interaction features to obtain the encoded features of the target vehicle (The driving feature vector of each of the other vehicles relative to the target vehicle is input into the interaction feature vector prediction network, to obtain an interaction feature vector between the another vehicle and the target vehicle. [0161]).
Regarding claim 9:
Li teaches all the limitations of claim 1, upon which this claim is dependent.
Li further teaches:
for each target vehicle of the plurality of target vehicles, structuring the perceived information to obtain structured data of the target vehicle (the driving information of the target vehicle or the surrounding vehicle includes information that can be sensed by the current vehicle and that affects a driving intention of the target vehicle, for example, location information, driving speed information, driving direction information, and head orientation information of the target vehicle and the vehicle around [0147]); and
adding semantic information to the structured data to obtain vectorized features of the target vehicle (intention types determined based on a structure of a map in which the target vehicle is located is not fixed, and the intention can effectively improve accuracy of a behavior description of the target vehicle. In addition, target vehicle intention prediction is converted into matching between a motion status of the target vehicle and the map information, which is different from fixed-type intention classification in the existing method. Further, the lane intention and the road intention of the target vehicle assist each other, to improve generalization and accuracy of predicting the driving intention of the target vehicle. [0020]).
Regarding claim 10:
Li teaches all the limitations of claim 2, upon which this claim is dependent.
Li further teaches:
for each target vehicle of the plurality of target vehicles, structuring the perceived information to obtain structured data of the target vehicle (the driving information of the target vehicle or the surrounding vehicle includes information that can be sensed by the current vehicle and that affects a driving intention of the target vehicle, for example, location information, driving speed information, driving direction information, and head orientation information of the target vehicle and the vehicle around [0147]); and
adding semantic information to the structured data to obtain vectorized features of the target vehicle (intention types determined based on a structure of a map in which the target vehicle is located is not fixed, and the intention can effectively improve accuracy of a behavior description of the target vehicle. In addition, target vehicle intention prediction is converted into matching between a motion status of the target vehicle and the map information, which is different from fixed-type intention classification in the existing method. Further, the lane intention and the road intention of the target vehicle assist each other, to improve generalization and accuracy of predicting the driving intention of the target vehicle. [0020]).
Regarding claim 11:
Li teaches all the limitations of claim 10, upon which this claim is dependent.
Li further teaches:
performing sequential processing on the vehicle trajectory of the target vehicle and the vehicle trajectory of each of the plurality of second surrounding vehicles (driving information of the target vehicle and driving information of a surrounding vehicle of the target vehicle are obtained [0144]) to obtain sequential data comprising a historical frame and a current frame of a preset length (extracting one or more of a location feature of each of the surrounding vehicles in a first coordinate system, a speed feature, and a head orientation feature, where an origin of the first coordinate system is a current location of the target vehicle, the first coordinate system is a rectangular coordinate system, a y-axis of the first coordinate system is parallel to a length direction of a vehicle body of the target vehicle, and a forward direction of the y-axis is consistent with a head orientation of the target vehicle [0149]);
and converting the sequential data into a coordinate system with a current frame position of the target vehicle as an origin so that the sequential data is used as the structured data of the target vehicle (interaction feature vector that is between the target vehicle at the current moment and the i.sup.th lane and that is obtained by using the MLP network through extraction on the interaction feature between the target vehicle at the current moment and the i.sup.th lane, h.sub.li.sup.t-1 represents an interaction feature implicit vector between the target vehicle at a previous moment and the i.sup.th lane, h.sub.li.sup.t represents an interaction feature implicit vector between the target vehicle at the current moment and the i.sup.th lane, g.sub.j.sup.t represents an interaction feature between the target vehicle at the current moment …[0174]).
Regarding claim 12:
Li teaches all the limitations of claim 11, upon which this claim is dependent.
Li further teaches:
segmenting a lane line in the road network information according to a preset distance to obtain a plurality of lane line segments (L.sub.j is a length of a virtual lane line segment, and j=0, 1, 2, 3, 4; L.sub.5 is a distance between an end point of a previous line segment and a target projection point; and |⋅| is a vector length. [0154]);
converting endpoint coordinates of the plurality of lane line segments to the coordinate system with the current frame position of the target vehicle as the origin (the interaction feature between the target vehicle and each lane may be extracted according to the following rule: extracting one or more of a location feature of a target vehicle in each third coordinate system, a feature of an angle formed by the head orientation of the target vehicle and a driving direction of the lane, and a feature that a location of the target vehicle in each third coordinate system, and the angle formed by the head orientation of the target vehicle and the driving orientation of the lane change with the driving moment, where each third coordinate system is a frenet coordinate system, a reference line of each third coordinate system is determined based on a center line of each lane, and an origin of each third coordinate system is determined based on an end point of the center line of each lane [0151]);
for the plurality of lane line segments in the coordinate system with the current frame position of the target vehicle as the origin, selecting lane line segments from the plurality of lane line segments according to a preset third rule (the interaction feature between the target vehicle and each road may be extracted according to the following rule: extracting one or more of a location feature of the target vehicle in each second coordinate system, a distance feature between the target vehicle and an origin, a head orientation feature of the target vehicle, and a feature that a location of the target vehicle in each second coordinate system, a distance between the target vehicle and the origin, and the head orientation of the target vehicle change with a driving moment, where each second coordinate system is a rectangular coordinate system, an origin of each second coordinate system is determined based on an exit location of each road, and an x-axis direction is determined based on a driving direction of each road [0156]); and
using endpoint coordinates of the lane line segments selected (a reference line of each third coordinate system is determined based on a center line of each lane, and an origin of each third coordinate system is determined based on an end point of the center line of each lane [0151]) as the structured data of the target vehicle (a driving feature of the target vehicle relative to each lane, namely, an interaction feature between the target vehicle and each lane, is determined based on the driving information of the target vehicle and the lane layer information [0150]).
Regarding claim 13:
Li teaches:
A control device (fig. 1, computer system 112), comprising at least one processor (fig. 1, processor 113) and at least one storage apparatus storing a plurality of program codes (fig. 1, memory 114), wherein the plurality of program codes are adapted to be loaded and executed by the at least one processor to perform a method for predicting a vehicle trajectory (a vehicle driving intention prediction method [0006]), wherein the method for predicting the vehicle trajectory comprises:
obtaining vectorized features of a plurality of target vehicles (to determine an interaction feature vector between the surrounding vehicle and the target vehicle, where the interaction feature vector between the surrounding vehicle and the target vehicle represents impact of the surrounding vehicle on the target vehicle [0010]; a driving feature vector of the target vehicle relative to each of the plurality of lanes [0013]) based on perceived information (The sensor system 104 may include several sensors that can sense information about the ambient environment of the vehicle 100. [0062]) of an autonomous vehicle (vehicle 100 operates in an autonomous mode [0084]), wherein the perceived information is obtained based on data acquired by a sensor of the autonomous vehicle (the sensor system 104 may include a positioning system 122 (the positioning system may be a Global Positioning System (GPS), a BeiDou system, or another positioning system), an inertial measurement unit (IMU) 124, a radar 126, a laser rangefinder 128, and a camera 130. The sensor system 104 may further include sensors (for example, an in-vehicle air quality monitor, a fuel gauge, and an oil temperature gauge) in an internal system of the vehicle 100. Sensor data from one or more of these sensors can be used to detect an object and corresponding features (a location, a shape, a direction, a speed, and the like). Such detection and recognition are key functions of safe operation of the vehicle 100 [0063]);
obtaining a trajectory prediction result (The prediction unit 43 predicts the behavior intention and the future track of the target vehicle [0122]) based on vectorized features of each vehicle of the plurality of target vehicles (based on current map information and the target information sensed by the sensing unit [0122]), to obtain a plurality of trajectory prediction results of the plurality of target vehicles (The target fusion unit 42, the prediction unit 43, the planning unit 43, and the control unit 45 are all implemented in the processor in FIG. 1 or FIG. 2. In a driving process of the vehicle, an intention of another vehicle is predicted in real time, accurately, and reliably, so that the vehicle can predict a traffic condition in front of the vehicle, and establish a traffic situation around the vehicle. This helps determine importance of the target of another surrounding vehicle of the vehicle, and filter a key target for interaction, so that the vehicle can plan the route in advance and safely pass through a complex road condition scenario. [0122]), wherein the plurality of target vehicles (extracting one or more of a location feature of each of the surrounding vehicles [0149]) comprise the autonomous vehicle (fig. 6, current vehicle) and a plurality of first surrounding vehicles (fig. 6, another vehicle), wherein the plurality of first surrounding vehicles being vehicles in a surrounding environment of the autonomous vehicle that are selected according to a preset first rule (The surrounding vehicle of the target vehicle may be understood as another vehicle that is at a specific distance from the target vehicle. The distance may be set by a user, or may be set by a skilled person, or may be related to a sensing distance of a sensor of the current vehicle. [0145]);
Controlling the autonomous vehicle based on the trajectory prediction result obtained (the application 141 may also be a program for controlling the autonomous vehicle to avoid collision with another vehicle and safely pass through an intersection [0102]).
Regarding claim 14:
Li teaches all the limitations of claim 13, upon which this claim is dependent.
Li further teaches:
wherein the obtaining vectorized features of the plurality of target vehicles (a driving feature vector of the target vehicle relative to each of the plurality of lanes [0013]) further comprises:
obtaining, based on the perceived information of the autonomous vehicle (the processor 113 may predict a driving track of another vehicle based on a surrounding road condition and another vehicle condition that are detected by the sensor 153 [0104]), self- vehicle trajectory vectorized features (The planning unit 44 plans a driving route of the vehicle based on a prediction result of the prediction unit and/or output information of the navigation unit 47 [0122]), surrounding trajectory vectorized features (the sensor 153 may detect an animal, a vehicle, an obstacle, or cross walk [0103]), and road network vectorized features of each target vehicle of the plurality of target vehicles (a driving feature implicit vector of the target vehicle relative to each of the plurality of lanes [0010]);
wherein the surrounding trajectory vectorized features are trajectory vectorized features of a plurality of second surrounding vehicles of a target vehicle of the plurality of target vehicles (a driving feature vector of each of the surrounding vehicles relative to the target vehicle [0012]), the plurality of second surrounding vehicles being vehicles in a surrounding environment of the target vehicle that are selected according to a preset second rule (The surrounding vehicle of the target vehicle may be understood as another vehicle that is at a specific distance from the target vehicle. The distance may be set by a user, or may be set by a skilled person, or may be related to a sensing distance of a sensor of the current vehicle. [0145]).
Regarding claim 15:
Li teaches all the limitations of claim 14, upon which this claim is dependent.
Li further teaches:
wherein the obtaining the trajectory prediction result (The prediction unit 43 predicts the behavior intention and the future track of the target vehicle [0122]) based on vectorized features of each target vehicle of the plurality of target vehicles (based on current map information and the target information sensed by the sensing unit [0122]) further comprises:
obtaining, for each target vehicle of the plurality of target vehicles, encoded (The target fusion unit 42 processes the environment information around the vehicle sensed by the sensing unit 41, and outputs obstacle target information [0122]) features of the target vehicle (obtaining driving information of the target vehicle [0007]) based on the self-vehicle trajectory vectorized features (The planning unit 44 plans a driving route of the vehicle based on a prediction result of the prediction unit and/or output information of the navigation unit 47 [0122]), the surrounding trajectory vectorized features (the lane intention of the target vehicle based on the driving feature of the surrounding vehicle relative to the target vehicle [0007]), and the road network vectorized features of the target vehicle (the driving feature of the target vehicle relative to each of the plurality of roads [0008]);
and obtaining the trajectory prediction result of the target vehicle based on the encoded features of the target vehicle (The prediction unit 43 predicts the behavior intention and the future track of the target vehicle based on current map information and the target information sensed by the sensing unit. [0122]).
Regarding claim 16:
Li teaches all the limitations of claim 15, upon which this claim is dependent.
Li further teaches:
wherein obtaining encoded features of the target vehicle based on the self-vehicle trajectory vectorized features (The target fusion unit 42 processes the environment information around the vehicle sensed by the sensing unit 41, and outputs obstacle target information [0122]), the surrounding trajectory vectorized features (the lane intention of the target vehicle based on the driving feature of the surrounding vehicle relative to the target vehicle [0007]), and the road network vectorized features of the target vehicle (the driving feature of the target vehicle relative to each of the plurality of roads [0008]) further comprises:
encoding the self-vehicle trajectory vectorized features, the surrounding trajectory vectorized features, and the road network vectorized features (The target fusion unit 42 processes the environment information around the vehicle sensed by the sensing unit 41, and outputs obstacle target information [0122]) to obtain self-vehicle trajectory encoded features, surrounding trajectory encoded features, and road network encoded features (The target fusion unit 42 processes the environment information around the vehicle sensed by the sensing unit 41, and outputs obstacle target information [0122]), respectively;
performing feature interaction (an interaction feature between the target vehicle and another vehicle, may be determined based on the driving information of the target vehicle and the driving information of the surrounding vehicle of the target vehicle [0148]) on the self-vehicle trajectory encoded features, the surrounding trajectory encoded features, and the road network encoded (The target fusion unit 42 processes the environment information around the vehicle sensed by the sensing unit 41, and outputs obstacle target information [0122]) features to obtain self-vehicle trajectory interaction features (the interaction feature between the target vehicle and the other vehicle [0157]), surrounding trajectory interaction features (the interaction feature between the target vehicle and each road [0157]), and environment interaction features (the interaction feature between the target vehicle and each lane [0157]), respectively ; and
performing feature fusion (The target fusion unit 42 processes the environment information around the vehicle sensed by the sensing unit 41 [0122]) on the self-vehicle trajectory interaction features, the surrounding trajectory interaction features, and the environment interaction features to obtain the encoded features of the target vehicle (The driving feature vector of each of the other vehicles relative to the target vehicle is input into the interaction feature vector prediction network, to obtain an interaction feature vector between the another vehicle and the target vehicle. [0161]).
Regarding claim 20:
Li teaches all the limitations of claim 13, upon which this claim is dependent.
Li further teaches:
A vehicle (fig. 1, vehicle 100), comprising the control device of claim 13 (fig. 1, controller 112).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 5-6 and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et. al. (US 2023/0399023), herein Li in view of Kuderer et. al. (Learning Driving Styles…)(NPL), herein Kuderer.
Regarding claim 5:
Li teaches all the limitations of claim 4, upon which this claim is dependent.
Li further teaches:
wherein performing feature interaction on the self-vehicle trajectory encoded features, the surrounding trajectory encoded features, and the road network encoded features (an interaction feature between the target vehicle and another vehicle, may be determined based on the driving information of the target vehicle and the driving information of the surrounding vehicle of the target vehicle [0148]) further comprises:
using, for the self-vehicle trajectory encoded features, [an all-ones vector] as environment information for feature interaction to obtain the self-vehicle trajectory interaction features (a driving feature of the target vehicle relative to each lane, namely, an interaction feature between the target vehicle and each lane, is determined based on the driving information of the target vehicle and the lane layer information. [0150]);
using, for the surrounding trajectory encoded features, the self-vehicle trajectory encoded features as environment information for feature interaction to obtain the surrounding trajectory interaction features (a driving feature of the target vehicle relative to each road, namely, an interaction feature between the target vehicle and each road, is determined based on the driving information of the target vehicle and the road layer information [0155]); and
using, for the road network encoded features, fusion features of the self-vehicle trajectory interaction features and the surrounding trajectory interaction features as environment information for feature interaction to obtain road network interaction features (a road intention of the target vehicle and a lane intention of the target vehicle are determined based on the interaction feature between the target vehicle and the another vehicle, the interaction feature between the target vehicle and each lane, and the interaction feature between the target vehicle and each road. [0158]).
Li does not explicitly teach, however Kuderer teaches:
an all-ones vector (The initial guess for the feature weights θ was an all-ones vector [page 2645])
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Li to include the teachings as taught by Kuderer with a reasonable expectation of success. Both arts are in the same field of endeavor of autonomous vehicle control. Kuderer teaches the benefit of “a learning
from demonstration approach that allows the user to simply demonstrate the desired style by driving the car manually. We model the individual style in terms of a cost function and use
feature-based inverse reinforcement learning to find the model parameters that fit the observed style best. Once the model has been learned, it can be used to efficiently compute trajectories for the vehicle in autonomous mode. We show that our approach is capable of learning cost functions and reproducing different driving styles using data from real drivers. [Kuderer, abstract]”.
Regarding claim 6:
Li and Kuderer teaches all the limitations of claim 5, upon which this claim is dependent.
Li further teaches:
performing feature fusion on the self-vehicle trajectory interaction features and the surrounding trajectory interaction features to obtain the fusion features (An algorithm of predicting the road intention of the target vehicle is as follows… h.sub.gj.sup.t represents an interaction feature implicit vector between the target vehicle at a current moment and a j.sup.th road, h.sub.li.sup.t represents an interaction feature implicit vector between the target vehicle and an i.sup.th lane, α.sub.ji represents a lane intention of the target vehicle corresponding to the i.sup.th lane, P.sub.j.sup.t represents a fusion vector of an interaction feature implicit vector between the i.sup.th lane and the target vehicle corresponding to all lanes associated with the j.sup.th road, and β.sub.j represents a probability that the target vehicle drives away from the intersection from the j.sup.th road, namely, the road intention of the target vehicle corresponding to the j.sup.th road, that is obtained after h.sub.gj.sup.t, and P.sub.j.sup.t are input into the road intention prediction subnetwork [0183]).
Regarding claim 17:
Li teaches all the limitations of claim 16, upon which this claim is dependent.
Li further teaches:
wherein performing feature interaction on the self-vehicle trajectory encoded features, the surrounding trajectory encoded features, and the road network encoded features (an interaction feature between the target vehicle and another vehicle, may be determined based on the driving information of the target vehicle and the driving information of the surrounding vehicle of the target vehicle [0148]) further comprises:
using, for the self-vehicle trajectory encoded features, [an all-ones vector] as environment information for feature interaction to obtain the self-vehicle trajectory interaction features (a driving feature of the target vehicle relative to each lane, namely, an interaction feature between the target vehicle and each lane, is determined based on the driving information of the target vehicle and the lane layer information. [0150]);
using, for the surrounding trajectory encoded features, the self-vehicle trajectory encoded features as environment information for feature interaction to obtain the surrounding trajectory interaction features (a driving feature of the target vehicle relative to each road, namely, an interaction feature between the target vehicle and each road, is determined based on the driving information of the target vehicle and the road layer information [0155]); and
using, for the road network encoded features, fusion features of the self-vehicle trajectory interaction features and the surrounding trajectory interaction features as environment information for feature interaction to obtain road network interaction features (a road intention of the target vehicle and a lane intention of the target vehicle are determined based on the interaction feature between the target vehicle and the another vehicle, the interaction feature between the target vehicle and each lane, and the interaction feature between the target vehicle and each road. [0158]).
Li does not explicitly teach, however Kuderer teaches:
an all-ones vector (The initial guess for the feature weights θ was an all-ones vector [page 2645])
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Li to include the teachings as taught by Kuderer with a reasonable expectation of success. Both arts are in the same field of endeavor of autonomous vehicle control. Kuderer teaches the benefit of “a learning
from demonstration approach that allows the user to simply demonstrate the desired style by driving the car manually. We model the individual style in terms of a cost function and use
feature-based inverse reinforcement learning to find the model parameters that fit the observed style best. Once the model has been learned, it can be used to efficiently compute trajectories for the vehicle in autonomous mode. We show that our approach is capable of learning cost functions and reproducing different driving styles using data from real drivers. [Kuderer, abstract]”.
Regarding claim 18:
Li and Kuderer teaches all the limitations of claim 17, upon which this claim is dependent.
Li further teaches:
performing feature fusion on the self-vehicle trajectory interaction features and the surrounding trajectory interaction features to obtain the fusion features (An algorithm of predicting the road intention of the target vehicle is as follows… h.sub.gj.sup.t represents an interaction feature implicit vector between the target vehicle at a current moment and a j.sup.th road, h.sub.li.sup.t represents an interaction feature implicit vector between the target vehicle and an i.sup.th lane, α.sub.ji represents a lane intention of the target vehicle corresponding to the i.sup.th lane, P.sub.j.sup.t represents a fusion vector of an interaction feature implicit vector between the i.sup.th lane and the target vehicle corresponding to all lanes associated with the j.sup.th road, and β.sub.j represents a probability that the target vehicle drives away from the intersection from the j.sup.th road, namely, the road intention of the target vehicle corresponding to the j.sup.th road, that is obtained after h.sub.gj.sup.t, and P.sub.j.sup.t are input into the road intention prediction subnetwork [0183]).
Claim(s) 7-8 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et. al. (US 2023/0399023), herein Li in view of Sahin et. al. (US 2025/0330286), herein Sahin.
Regarding claim 7:
Li teaches all the limitations of claim 3, upon which this claim is dependent.
Li further teaches:
obtaining multimodal features of the target vehicle based on the encoded features of the target vehicle (This application provides a vehicle driving intention prediction method. A road intention and a lane intention of a target vehicle are first determined based on [0129]); and
obtaining the trajectory prediction result of the target vehicle based on the multimodal features (a driving feature of a surrounding vehicle relative to the target vehicle, a driving feature of the target vehicle relative to a road, and a driving feature of the target vehicle relative to a lane, and then a driving intention of the target vehicle is determined based on the road intention and the lane intention of the target vehicle. The driving intention of the target vehicle is determined by predicting a multi-level intention (namely, the lane intention and the road intention) of the target vehicle [0129]).
Li does not explicitly teach, however Sahin teaches:
using pre-learned anchor features as environment information (the RSU/anchor 102A may identify one or more additional RSU(s) along the future predicted trajectory of the detected UE 104A and activate one or more additional SL PRS transmissions [0057] [page 5 of provisional]), and
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Li to include the teachings as taught by Sahin with a reasonable expectation of success. Both arts are in the same field of endeavor of autonomous vehicle control. Sahin teaches the benefits of “the network or anchors can proactively configure SL PRS transmissions to be activated. In one example, the proactive configuration can be based on UE density, directivity, etc. or any other information carried via the SL transmissions of the detected one or more UE(s). In another example, the proactive configuration can be based on receipt of messages explicitly indicating the positioning request of the one or more UE(s). The proactive configuration will illustratively include additional anchors (one or more) along the future predicted trajectory of the detected UEs [Sahin, 0027]”.
Regarding claim 8:
Li teaches all the limitations of claim 4, upon which this claim is dependent.
Li further teaches:
obtaining multimodal features of the target vehicle based on the encoded features of the target vehicle (This application provides a vehicle driving intention prediction method. A road intention and a lane intention of a target vehicle are first determined based on [0129]); and
obtaining the trajectory prediction result of the target vehicle based on the multimodal features (a driving feature of a surrounding vehicle relative to the target vehicle, a driving feature of the target vehicle relative to a road, and a driving feature of the target vehicle relative to a lane, and then a driving intention of the target vehicle is determined based on the road intention and the lane intention of the target vehicle. The driving intention of the target vehicle is determined by predicting a multi-level intention (namely, the lane intention and the road intention) of the target vehicle [0129]).
Li does not explicitly teach, however Sahin teaches:
using pre-learned anchor features as environment information (the RSU/anchor 102A may identify one or more additional RSU(s) along the future predicted trajectory of the detected UE 104A and activate one or more additional SL PRS transmissions [0057] [page 5 of provisional]), and
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Li to include the teachings as taught by Sahin with a reasonable expectation of success. Both arts are in the same field of endeavor of autonomous vehicle control. Sahin teaches the benefits of “the network or anchors can proactively configure SL PRS transmissions to be activated. In one example, the proactive configuration can be based on UE density, directivity, etc. or any other information carried via the SL transmissions of the detected one or more UE(s). In another example, the proactive configuration can be based on receipt of messages explicitly indicating the positioning request of the one or more UE(s). The proactive configuration will illustratively include additional anchors (one or more) along the future predicted trajectory of the detected UEs [Sahin, 0027]”.
Regarding claim 19:
Li teaches all the limitations of claim 15, upon which this claim is dependent.
Li further teaches:
obtaining multimodal features of the target vehicle based on the encoded features of the target vehicle (This application provides a vehicle driving intention prediction method. A road intention and a lane intention of a target vehicle are first determined based on [0129]); and
obtaining the trajectory prediction result of the target vehicle based on the multimodal features (a driving feature of a surrounding vehicle relative to the target vehicle, a driving feature of the target vehicle relative to a road, and a driving feature of the target vehicle relative to a lane, and then a driving intention of the target vehicle is determined based on the road intention and the lane intention of the target vehicle. The driving intention of the target vehicle is determined by predicting a multi-level intention (namely, the lane intention and the road intention) of the target vehicle [0129]).
Li does not explicitly teach, however Sahin teaches:
using pre-learned anchor features as environment information (the RSU/anchor 102A may identify one or more additional RSU(s) along the future predicted trajectory of the detected UE 104A and activate one or more additional SL PRS transmissions [0057] [page 5 of provisional]), and
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Li to include the teachings as taught by Sahin with a reasonable expectation of success. Both arts are in the same field of endeavor of autonomous vehicle control. Sahin teaches the benefits of “the network or anchors can proactively configure SL PRS transmissions to be activated. In one example, the proactive configuration can be based on UE density, directivity, etc. or any other information carried via the SL transmissions of the detected one or more UE(s). In another example, the proactive configuration can be based on receipt of messages explicitly indicating the positioning request of the one or more UE(s). The proactive configuration will illustratively include additional anchors (one or more) along the future predicted trajectory of the detected UEs [Sahin, 0027]”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang (US 11,798,407) discloses A method and system for identifying a lane changing intention of a manually driven vehicle are disclosed. The method includes: preprocessing a preset vehicle trajectory data set; extracting vehicle traveling features and driving behavior features of a target vehicle; constructing a vehicle following and lane changing decision prediction model based on machine learning, and inputting the preprocessed vehicle trajectory data set into the prediction model for training; obtaining a speed, an acceleration and a vehicle head distance of the target vehicle according to the vehicle traveling features of the target vehicle, and obtaining a large vehicle feature value and a clustering feature value according to the driving behavior features of the target vehicle; and inputting the speed, the acceleration, the vehicle head distance, the large vehicle feature value and the clustering feature value into the trained prediction model to obtain a lane changing intention identification result of the target vehicle.
Li (US 2024/0182079) discloses An autonomous vehicle path prediction method includes sensing vehicles driving on a road and generating sensing signals corresponding to the vehicles; surrounding vehicles and a current state of the emergency vehicle and performing an emergency path prediction corresponding to the emergency vehicle when the vehicles further include the emergency vehicle; generating an emergency autonomous driving decision according to the emergency path prediction and providing an autonomous vehicle path planning corresponding to the emergency autonomous driving decision; and controlling the autonomous vehicle to change the driving path and the driving mode on the road according to the autonomous vehicle path planning.
Sapp (US 12,325,452) discloses Methods, systems, and apparatus, including computer programs encoded on computer storage media, for agent trajectory prediction using candidate future intents.
Ansari (US 10,156,850) discloses Systems and methods for determining object motion and controlling autonomous vehicles are provided. In one example embodiment, a computing system can be configured to perform operations. The operations can include obtaining data indicative of state(s) of a first object and a second object within a surrounding environment of an autonomous vehicle. The operations can include determining a first predicted motion trajectory of the first object based at least in part on the state data. The operations can include determining a second predicted motion trajectory of the second object based at least in part on the state data and the first predicted motion trajectory of the first object. The operations can include determining a motion plan for the autonomous vehicle based at least in part on the second predicted motion trajectory of the second object and initiating a motion control in accordance with at least a portion of the motion plan.
Kurutach (US 2024/0246537) discloses autonomous vehicle (AV) training and, more specifically, to AV prediction layer training. In some aspects, the present disclosure provides a process for receiving road data representing a real-world environment encountered by an AV and generating, using a prediction layer of the AV, a predicted trajectory of a target vehicle, wherein the predicted trajectory comprises one or more waypoints and wherein the predicted trajectory is based on the road data. In some aspects, the process can further include steps for calculating a distance metric for the predicted trajectory, wherein the distance metric is based on a distance between the one or more waypoints and one or more corresponding drivable areas and updating the prediction layer of the AV based on the distance metric. Systems and machine-readable media are also provided.
Pronovost (US 2024/0217548) discloses a machine learning attention mechanism to predict movements, states, and/or trajectories of agents in various environments. In various examples, a prediction component of an autonomous vehicle may analyze sensor data to determine, for individual agents in the environment, unique sets of additional objects that are relevant to predicting the subsequent movements of the individual agents. For a particular agent, the prediction component may determine the relative positions and/or states between the agent and the associated set of relevant objects for the agent, and may use an attention mechanism to determine an object interaction vector including weighted attention scores for each additional object relative to the agent. Object interaction vectors may be generated for any number of agents and/or any number of timesteps to determine predicted agent movements and to forecast subsequent driving scenes within the environment.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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Scott R. Jagolinzer
Examiner
Art Unit 3665
/S.R.J./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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