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 .
Drawings
Figure 3 is objected to because it does not provide sufficient information to convey the invention or an aspect of the invention to the reader. The drawing is a series of numbers within a flowchart. While this drawing may be useful in conjunction with the specification, it provides no information without the specification. Figures 1 and 2 do not necessarily need descriptions because the figures themselves provide context, but Figure 3 is unreadable without the specification at hand. Flowcharts should be conveying the steps without needing the specification on hand. As such, the figure is objected to.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
Claims 1-3, 5, 7, 11, and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al (US Pub 2025/0033661 A1), hereafter known as Wang.
For Claim 1, Wang teaches A method for path planning for a vehicle, the method comprising:
determining a repulsive potential at each of a plurality of location points in an environment surrounding the vehicle using a vehicle perception sensor; ([0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1):)
[0019] Although illustrated as a passenger truck, the vehicle 102 can represent other types of motorized vehicles (e.g., a car, an automobile, a motorcycle, a bus, a tractor, a semi-trailer truck), watercraft (e.g., a boat), or aircraft (e.g., an airplane). The vehicle 102 includes one or more sensors 118 and the parking system 120. In the depicted environment 100, the sensors 118 are mounted to, or integrated within, front, central, and rear portions of the vehicle 102. As described in greater detail below, the sensors 118 may include camera systems, radar systems, lidar systems, ultrasonic systems, and positioning systems. The sensors 118 can provide sensor data regarding the stationary objects 112 and moving objects 114 to the parking system 120 (e.g., as an obstacle map).
[0020] In addition, the parking system 120 or another component of the vehicle 102 can use the sensors 118 to obtain an initial pose 104 and/or a goal pose 106 of the vehicle 102 (e.g., to park in the available space 110). The sensors 118 can also be used to generate an obstacle map for the environment 100 that includes the stationary objects 112 and moving objects 114.)
determining an attractive potential at each of the plurality of location points in the environment surrounding the vehicle using the vehicle perception sensor; (([0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1):)
[0019] Although illustrated as a passenger truck, the vehicle 102 can represent other types of motorized vehicles (e.g., a car, an automobile, a motorcycle, a bus, a tractor, a semi-trailer truck), watercraft (e.g., a boat), or aircraft (e.g., an airplane). The vehicle 102 includes one or more sensors 118 and the parking system 120. In the depicted environment 100, the sensors 118 are mounted to, or integrated within, front, central, and rear portions of the vehicle 102. As described in greater detail below, the sensors 118 may include camera systems, radar systems, lidar systems, ultrasonic systems, and positioning systems. The sensors 118 can provide sensor data regarding the stationary objects 112 and moving objects 114 to the parking system 120 (e.g., as an obstacle map).
[0020] In addition, the parking system 120 or another component of the vehicle 102 can use the sensors 118 to obtain an initial pose 104 and/or a goal pose 106 of the vehicle 102 (e.g., to park in the available space 110). The sensors 118 can also be used to generate an obstacle map for the environment 100 that includes the stationary objects 112 and moving objects 114.))
calculating a potential field representing the environment surrounding the vehicle based at least in part on the attractive potential at each of the plurality of location points and the repulsive potential at each of the plurality of location points, wherein the potential field quantifies a suitability of each of the plurality of location points in the environment for inclusion in a path for the vehicle; and (([0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1):)
[0019] Although illustrated as a passenger truck, the vehicle 102 can represent other types of motorized vehicles (e.g., a car, an automobile, a motorcycle, a bus, a tractor, a semi-trailer truck), watercraft (e.g., a boat), or aircraft (e.g., an airplane). The vehicle 102 includes one or more sensors 118 and the parking system 120. In the depicted environment 100, the sensors 118 are mounted to, or integrated within, front, central, and rear portions of the vehicle 102. As described in greater detail below, the sensors 118 may include camera systems, radar systems, lidar systems, ultrasonic systems, and positioning systems. The sensors 118 can provide sensor data regarding the stationary objects 112 and moving objects 114 to the parking system 120 (e.g., as an obstacle map).
[0020] In addition, the parking system 120 or another component of the vehicle 102 can use the sensors 118 to obtain an initial pose 104 and/or a goal pose 106 of the vehicle 102 (e.g., to park in the available space 110). The sensors 118 can also be used to generate an obstacle map for the environment 100 that includes the stationary objects 112 and moving objects 114.)
Figures 5-7)
generating the path for the vehicle based at least in part on the potential field. ([0034] At step 304, the parking system 120 or the motion planner 122 obtains inputs 302 and runs a parking trajectory algorithm in a 3D search space with a space-time artificial potential field. The 3D search space includes two positional dimensions and a time dimension. The inputs 302 include an initial or current pose of the vehicle 102, a goal pose near a selected parking space or destination within the parking environment, and an obstacle map. The initial pose may represent a source node for the parking trajectory algorithm and may be obtained from the localization system 208, which uses location data to determine the vehicle's location. The goal pose may represent a goal node for the parking trajectory algorithm and may be obtained from the parking space selector 212 or another system of the parking system 120. The parking system 120 may also obtain the obstacle map for the environment near, around, and including the initial pose and the goal pose. The obstacle map may be obtained from the perception system 210, which uses sensor data to generate and populate the obstacle map. In some implementations, the obstacle map can be a radar occupancy grid map generated from radar data or a similar type of occupancy grid map (e.g., an occupancy grid map that fuses data from multiple types of sensors).)
For Claim 2, Wang teaches The method of claim 1, wherein determining the repulsive potential at each of the plurality of location points further comprises:
detecting a plurality of obstacles using the vehicle perception sensor; ([0019] Although illustrated as a passenger truck, the vehicle 102 can represent other types of motorized vehicles (e.g., a car, an automobile, a motorcycle, a bus, a tractor, a semi-trailer truck), watercraft (e.g., a boat), or aircraft (e.g., an airplane). The vehicle 102 includes one or more sensors 118 and the parking system 120. In the depicted environment 100, the sensors 118 are mounted to, or integrated within, front, central, and rear portions of the vehicle 102. As described in greater detail below, the sensors 118 may include camera systems, radar systems, lidar systems, ultrasonic systems, and positioning systems. The sensors 118 can provide sensor data regarding the stationary objects 112 and moving objects 114 to the parking system 120 (e.g., as an obstacle map).
[0020] In addition, the parking system 120 or another component of the vehicle 102 can use the sensors 118 to obtain an initial pose 104 and/or a goal pose 106 of the vehicle 102 (e.g., to park in the available space 110). The sensors 118 can also be used to generate an obstacle map for the environment 100 that includes the stationary objects 112 and moving objects 114.)
measuring a distance between each of the plurality of location points and each of the plurality of obstacles using the vehicle perception sensor; and ([0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1):
[0073] At step 804, an obstacle map for an environment that includes the initial pose and the goal pose is obtained. For example, the motion planner 122 can obtain the obstacle map from a perception system. The obstacle map can be a radar occupancy grid map or a radar-centric occupancy grid map for the environment 100. The obstacles in the obstacle map can be represented by bounding boxes, circles, occupancy grids, free-space polygons, or any combination thereof. The motion planner 122 may also obtain a reference path.)
calculating the repulsive potential at each of the plurality of location points based at least in part on the distance between each of the plurality of location points and each of the plurality of obstacles. (([0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1):) )
For Claim 3, Wang teaches The method of claim 2, wherein detecting the plurality of obstacles further comprises:
detecting the plurality of obstacles using the vehicle perception sensor, wherein at least one of the plurality of obstacles is a marker, barrier, or road sign indicating a construction zone. ([0018] FIG. 1 illustrates an example environment 100 in which a parking system 120 of a vehicle 102 (e.g., a host vehicle, an ego vehicle) performs trajectory planning in a 3D search space with a space-time artificial potential field in accordance with the techniques of this disclosure. In the depicted environment 100, the vehicle 102 is in a parking lot or other environment with multiple parking spaces. In other implementations, the environment 100 may be a roadway with parking spaces off to the side of the roadway. The parking spaces are illustrated in FIG. 1 as parallel to the travel path of the vehicle 102. The parking spaces may also be at an angle or perpendicular to the travel path of the vehicle 102. The environment 100 includes stationary objects 112 (e.g., other vehicles parked in some of the parking spaces, pillars, curbs, and barriers) and moving objects 114 (e.g., pedestrians and other vehicles navigating the environment 100). Environment 100 may also include an available space 110 that is unoccupied.)
For Claim 5, Wang teaches The method of claim 1, wherein determining the attractive potential at each of the plurality of location points further comprises:
determining a goal location in the environment; ([0034] At step 304, the parking system 120 or the motion planner 122 obtains inputs 302 and runs a parking trajectory algorithm in a 3D search space with a space-time artificial potential field. The 3D search space includes two positional dimensions and a time dimension. The inputs 302 include an initial or current pose of the vehicle 102, a goal pose near a selected parking space or destination within the parking environment, and an obstacle map. The initial pose may represent a source node for the parking trajectory algorithm and may be obtained from the localization system 208, which uses location data to determine the vehicle's location. The goal pose may represent a goal node for the parking trajectory algorithm and may be obtained from the parking space selector 212 or another system of the parking system 120. The parking system 120 may also obtain the obstacle map for the environment near, around, and including the initial pose and the goal pose. The obstacle map may be obtained from the perception system 210, which uses sensor data to generate and populate the obstacle map. In some implementations, the obstacle map can be a radar occupancy grid map generated from radar data or a similar type of occupancy grid map (e.g., an occupancy grid map that fuses data from multiple types of sensors).
determining a distance between each of the plurality of location points and the goal location; and ([0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1): )
calculating the attractive potential at each of the plurality of location points based at least in part on the distance between each of the plurality of location points and the goal location. ( [0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1):) )
For Claim 7, Wang teaches The method of claim 1, wherein calculating the potential field further comprises:
determining a repulsive force at each of the plurality of location points based at least in part on the repulsive potential at each of the plurality of location points; ([0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1):
[00001]Utotal(s,l,t)=Uattractive(s,l,t)+Urepulsive(s,l,t)(1))
determining an attractive force at each of the plurality of location points based at least in part on the attractive potential at each of the plurality of location points; and ([0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1):
[00001]Utotal(s,l,t)=Uattractive(s,l,t)+Urepulsive(s,l,t)(1))
calculating the potential field based at least in part on the repulsive force at each of the plurality of location points and the attractive force at each of the plurality of location points. ([0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1):
[00001]Utotal(s,l,t)=Uattractive(s,l,t)+Urepulsive(s,l,t)(1))
For Claim 11, Wang teaches A system for path planning for a vehicle, the system comprising:
a vehicle perception sensor; ([0019] Although illustrated as a passenger truck, the vehicle 102 can represent other types of motorized vehicles (e.g., a car, an automobile, a motorcycle, a bus, a tractor, a semi-trailer truck), watercraft (e.g., a boat), or aircraft (e.g., an airplane). The vehicle 102 includes one or more sensors 118 and the parking system 120. In the depicted environment 100, the sensors 118 are mounted to, or integrated within, front, central, and rear portions of the vehicle 102. As described in greater detail below, the sensors 118 may include camera systems, radar systems, lidar systems, ultrasonic systems, and positioning systems. The sensors 118 can provide sensor data regarding the stationary objects 112 and moving objects 114 to the parking system 120 (e.g., as an obstacle map).)
a controller in electrical communication with the vehicle perception sensor, wherein the controller is programmed to: ([0025] FIG. 2 illustrates an example configuration of the vehicle 102 with the parking system 120 that performs trajectory planning in a 3D search space with a space-time artificial potential field. As described in FIG. 1, the vehicle 102 includes the sensors 118 and the parking system 120, which may include a parking space selector 212, a global planner 214, the motion planner 122, and a parking planner 216. In addition, the vehicle 102 may include one or more communication devices 202, one or more processors 204, computer-readable storage media (CRM) 206, and a control interface 218 to one or more vehicle-based systems, including one or more autonomous-driving systems 220.)
determine a repulsive potential at each of a plurality of location points in an environment surrounding the vehicle using a vehicle perception sensor; (([0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1):)
[0019] Although illustrated as a passenger truck, the vehicle 102 can represent other types of motorized vehicles (e.g., a car, an automobile, a motorcycle, a bus, a tractor, a semi-trailer truck), watercraft (e.g., a boat), or aircraft (e.g., an airplane). The vehicle 102 includes one or more sensors 118 and the parking system 120. In the depicted environment 100, the sensors 118 are mounted to, or integrated within, front, central, and rear portions of the vehicle 102. As described in greater detail below, the sensors 118 may include camera systems, radar systems, lidar systems, ultrasonic systems, and positioning systems. The sensors 118 can provide sensor data regarding the stationary objects 112 and moving objects 114 to the parking system 120 (e.g., as an obstacle map).
[0020] In addition, the parking system 120 or another component of the vehicle 102 can use the sensors 118 to obtain an initial pose 104 and/or a goal pose 106 of the vehicle 102 (e.g., to park in the available space 110). The sensors 118 can also be used to generate an obstacle map for the environment 100 that includes the stationary objects 112 and moving objects 114.))
determine an attractive potential at each of the plurality of location points in the environment surrounding the vehicle using the vehicle perception sensor; ((([0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1):)
[0019] Although illustrated as a passenger truck, the vehicle 102 can represent other types of motorized vehicles (e.g., a car, an automobile, a motorcycle, a bus, a tractor, a semi-trailer truck), watercraft (e.g., a boat), or aircraft (e.g., an airplane). The vehicle 102 includes one or more sensors 118 and the parking system 120. In the depicted environment 100, the sensors 118 are mounted to, or integrated within, front, central, and rear portions of the vehicle 102. As described in greater detail below, the sensors 118 may include camera systems, radar systems, lidar systems, ultrasonic systems, and positioning systems. The sensors 118 can provide sensor data regarding the stationary objects 112 and moving objects 114 to the parking system 120 (e.g., as an obstacle map).
[0020] In addition, the parking system 120 or another component of the vehicle 102 can use the sensors 118 to obtain an initial pose 104 and/or a goal pose 106 of the vehicle 102 (e.g., to park in the available space 110). The sensors 118 can also be used to generate an obstacle map for the environment 100 that includes the stationary objects 112 and moving objects 114.)))
calculate a potential field representing the environment surrounding the vehicle based at least in part on the attractive potential at each of the plurality of location points and the repulsive potential at each of the plurality of location points, wherein the potential field quantifies a suitability of each of the plurality of location points in the environment for inclusion in a path for the vehicle; and ((([0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1):)
[0019] Although illustrated as a passenger truck, the vehicle 102 can represent other types of motorized vehicles (e.g., a car, an automobile, a motorcycle, a bus, a tractor, a semi-trailer truck), watercraft (e.g., a boat), or aircraft (e.g., an airplane). The vehicle 102 includes one or more sensors 118 and the parking system 120. In the depicted environment 100, the sensors 118 are mounted to, or integrated within, front, central, and rear portions of the vehicle 102. As described in greater detail below, the sensors 118 may include camera systems, radar systems, lidar systems, ultrasonic systems, and positioning systems. The sensors 118 can provide sensor data regarding the stationary objects 112 and moving objects 114 to the parking system 120 (e.g., as an obstacle map).
[0020] In addition, the parking system 120 or another component of the vehicle 102 can use the sensors 118 to obtain an initial pose 104 and/or a goal pose 106 of the vehicle 102 (e.g., to park in the available space 110). The sensors 118 can also be used to generate an obstacle map for the environment 100 that includes the stationary objects 112 and moving objects 114.)
Figures 5-7)
[0035] The parking trajectory algorithm utilizes space-time artificial potential fields and a graph-search based algorithm to plan the trajectory 306 of the vehicle 102 in the parking environment. The parking trajectory algorithm first discretizes the 3D search space into an array of 3D nodes and assigns artificial potential field values or magnitudes to each node using potential field functions. The parking trajectory algorithm determines a trajectory that travels from the current position (e.g., the initial pose) to the goal position with the lowest cost or potential.)
generate the path for the vehicle based at least in part on the potential field. (([0034] At step 304, the parking system 120 or the motion planner 122 obtains inputs 302 and runs a parking trajectory algorithm in a 3D search space with a space-time artificial potential field. The 3D search space includes two positional dimensions and a time dimension. The inputs 302 include an initial or current pose of the vehicle 102, a goal pose near a selected parking space or destination within the parking environment, and an obstacle map. The initial pose may represent a source node for the parking trajectory algorithm and may be obtained from the localization system 208, which uses location data to determine the vehicle's location. The goal pose may represent a goal node for the parking trajectory algorithm and may be obtained from the parking space selector 212 or another system of the parking system 120. The parking system 120 may also obtain the obstacle map for the environment near, around, and including the initial pose and the goal pose. The obstacle map may be obtained from the perception system 210, which uses sensor data to generate and populate the obstacle map. In some implementations, the obstacle map can be a radar occupancy grid map generated from radar data or a similar type of occupancy grid map (e.g., an occupancy grid map that fuses data from multiple types of sensors).))
For Claim 18, Wang teaches A method for path planning for a vehicle, the method comprising:
detecting a plurality of obstacles using a vehicle perception sensor; (([0019] Although illustrated as a passenger truck, the vehicle 102 can represent other types of motorized vehicles (e.g., a car, an automobile, a motorcycle, a bus, a tractor, a semi-trailer truck), watercraft (e.g., a boat), or aircraft (e.g., an airplane). The vehicle 102 includes one or more sensors 118 and the parking system 120. In the depicted environment 100, the sensors 118 are mounted to, or integrated within, front, central, and rear portions of the vehicle 102. As described in greater detail below, the sensors 118 may include camera systems, radar systems, lidar systems, ultrasonic systems, and positioning systems. The sensors 118 can provide sensor data regarding the stationary objects 112 and moving objects 114 to the parking system 120 (e.g., as an obstacle map).
[0020] In addition, the parking system 120 or another component of the vehicle 102 can use the sensors 118 to obtain an initial pose 104 and/or a goal pose 106 of the vehicle 102 (e.g., to park in the available space 110). The sensors 118 can also be used to generate an obstacle map for the environment 100 that includes the stationary objects 112 and moving objects 114.))
measuring a distance between each of a plurality of location points and each of a plurality of obstacles in an environment surrounding the vehicle using the vehicle perception sensor; (([0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1):
[0073] At step 804, an obstacle map for an environment that includes the initial pose and the goal pose is obtained. For example, the motion planner 122 can obtain the obstacle map from a perception system. The obstacle map can be a radar occupancy grid map or a radar-centric occupancy grid map for the environment 100. The obstacles in the obstacle map can be represented by bounding boxes, circles, occupancy grids, free-space polygons, or any combination thereof. The motion planner 122 may also obtain a reference path.))
calculating a repulsive potential at each of the plurality of location points based at least in part on the distance between each of the plurality of location points and each of the plurality of obstacles; (([0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1):)
[0019] Although illustrated as a passenger truck, the vehicle 102 can represent other types of motorized vehicles (e.g., a car, an automobile, a motorcycle, a bus, a tractor, a semi-trailer truck), watercraft (e.g., a boat), or aircraft (e.g., an airplane). The vehicle 102 includes one or more sensors 118 and the parking system 120. In the depicted environment 100, the sensors 118 are mounted to, or integrated within, front, central, and rear portions of the vehicle 102. As described in greater detail below, the sensors 118 may include camera systems, radar systems, lidar systems, ultrasonic systems, and positioning systems. The sensors 118 can provide sensor data regarding the stationary objects 112 and moving objects 114 to the parking system 120 (e.g., as an obstacle map).
[0020] In addition, the parking system 120 or another component of the vehicle 102 can use the sensors 118 to obtain an initial pose 104 and/or a goal pose 106 of the vehicle 102 (e.g., to park in the available space 110). The sensors 118 can also be used to generate an obstacle map for the environment 100 that includes the stationary objects 112 and moving objects 114.))
determining a goal location in the environment; (([0034] At step 304, the parking system 120 or the motion planner 122 obtains inputs 302 and runs a parking trajectory algorithm in a 3D search space with a space-time artificial potential field. The 3D search space includes two positional dimensions and a time dimension. The inputs 302 include an initial or current pose of the vehicle 102, a goal pose near a selected parking space or destination within the parking environment, and an obstacle map. The initial pose may represent a source node for the parking trajectory algorithm and may be obtained from the localization system 208, which uses location data to determine the vehicle's location. The goal pose may represent a goal node for the parking trajectory algorithm and may be obtained from the parking space selector 212 or another system of the parking system 120. The parking system 120 may also obtain the obstacle map for the environment near, around, and including the initial pose and the goal pose. The obstacle map may be obtained from the perception system 210, which uses sensor data to generate and populate the obstacle map. In some implementations, the obstacle map can be a radar occupancy grid map generated from radar data or a similar type of occupancy grid map (e.g., an occupancy grid map that fuses data from multiple types of sensors).)
determining a distance between each of the plurality of location points and the goal location; (([0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1): ))
calculating an attractive potential at each of the plurality of location points based at least in part on the distance between each of the plurality of location points and the goal location; (( [0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1):) ))
calculating a potential field representing the environment surrounding the vehicle based at least in part on the attractive potential at each of the plurality of location points and the repulsive potential at each of the plurality of location points, wherein the potential field quantifies a suitability of each of the plurality of location points in the environment for inclusion in a path for the vehicle; and (((([0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1):)
[0019] Although illustrated as a passenger truck, the vehicle 102 can represent other types of motorized vehicles (e.g., a car, an automobile, a motorcycle, a bus, a tractor, a semi-trailer truck), watercraft (e.g., a boat), or aircraft (e.g., an airplane). The vehicle 102 includes one or more sensors 118 and the parking system 120. In the depicted environment 100, the sensors 118 are mounted to, or integrated within, front, central, and rear portions of the vehicle 102. As described in greater detail below, the sensors 118 may include camera systems, radar systems, lidar systems, ultrasonic systems, and positioning systems. The sensors 118 can provide sensor data regarding the stationary objects 112 and moving objects 114 to the parking system 120 (e.g., as an obstacle map).
[0020] In addition, the parking system 120 or another component of the vehicle 102 can use the sensors 118 to obtain an initial pose 104 and/or a goal pose 106 of the vehicle 102 (e.g., to park in the available space 110). The sensors 118 can also be used to generate an obstacle map for the environment 100 that includes the stationary objects 112 and moving objects 114.)
Figures 5-7)
[0035] The parking trajectory algorithm utilizes space-time artificial potential fields and a graph-search based algorithm to plan the trajectory 306 of the vehicle 102 in the parking environment. The parking trajectory algorithm first discretizes the 3D search space into an array of 3D nodes and assigns artificial potential field values or magnitudes to each node using potential field functions. The parking trajectory algorithm determines a trajectory that travels from the current position (e.g., the initial pose) to the goal position with the lowest cost or potential.))
generating the path for the vehicle based at least in part on the potential field. (([0034] At step 304, the parking system 120 or the motion planner 122 obtains inputs 302 and runs a parking trajectory algorithm in a 3D search space with a space-time artificial potential field. The 3D search space includes two positional dimensions and a time dimension. The inputs 302 include an initial or current pose of the vehicle 102, a goal pose near a selected parking space or destination within the parking environment, and an obstacle map. The initial pose may represent a source node for the parking trajectory algorithm and may be obtained from the localization system 208, which uses location data to determine the vehicle's location. The goal pose may represent a goal node for the parking trajectory algorithm and may be obtained from the parking space selector 212 or another system of the parking system 120. The parking system 120 may also obtain the obstacle map for the environment near, around, and including the initial pose and the goal pose. The obstacle map may be obtained from the perception system 210, which uses sensor data to generate and populate the obstacle map. In some implementations, the obstacle map can be a radar occupancy grid map generated from radar data or a similar type of occupancy grid map (e.g., an occupancy grid map that fuses data from multiple types of sensors).))
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 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in light of Abdelbasit et al (Please refer to attached IDS).
For Claim 4, Wang teaches The method of claim 2,
does not teach wherein calculating the repulsive potential at each of the plurality of location points further comprises:
calculating the repulsive potential at each of the plurality of location points, wherein the repulsive potential at each of the plurality of location points is defined by a repulsive potential function
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wherein Ureps is the repulsive potential function, s is a vector describing a location of one of the plurality of location points, kr is a predetermined repulsive constant, ρi is a distance between the location of the one of the plurality of location points and an ith obstacle of the plurality of obstacles, ρ0,i is a minimum allowed distance between the vehicle and the ith obstacle of the plurality of obstacles, and a summation operator Σ indicates a summation over each of the plurality of obstacles.
Abdelbasit, however, does teach wherein calculating the repulsive potential at each of the plurality of location points further comprises:
calculating the repulsive potential at each of the plurality of location points, wherein the repulsive potential at each of the plurality of location points is defined by a repulsive potential function
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102
461
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wherein Ureps is the repulsive potential function, s is a vector describing a location of one of the plurality of location points, kr is a predetermined repulsive constant, ρi is a distance between the location of the one of the plurality of location points and an ith obstacle of the plurality of obstacles, ρ0,i is a minimum allowed distance between the vehicle and the ith obstacle of the plurality of obstacles, and a summation operator Σ indicates a summation over each of the plurality of obstacles.
(Page 91, Column 2
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Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify in light of Abdelbasit such that the repulsive field is determined by this equation because in magnetism and other forms of repulsion, the relationship between strength of the field and the distance to the object creating the field is often a square of the distance. This would allow stronger forces near the object, and weaker forces further away, which would encourage the vehicle to maintain a distance.
For Claim 12, Wang teaches The system of claim 11, wherein to determine the repulsive potential, the controller is further programmed to:
detect a plurality of obstacles using the vehicle perception sensor, wherein at least one of the plurality of obstacles is a marker, barrier, or road sign indicating a construction zone; (([0019] Although illustrated as a passenger truck, the vehicle 102 can represent other types of motorized vehicles (e.g., a car, an automobile, a motorcycle, a bus, a tractor, a semi-trailer truck), watercraft (e.g., a boat), or aircraft (e.g., an airplane). The vehicle 102 includes one or more sensors 118 and the parking system 120. In the depicted environment 100, the sensors 118 are mounted to, or integrated within, front, central, and rear portions of the vehicle 102. As described in greater detail below, the sensors 118 may include camera systems, radar systems, lidar systems, ultrasonic systems, and positioning systems. The sensors 118 can provide sensor data regarding the stationary objects 112 and moving objects 114 to the parking system 120 (e.g., as an obstacle map).
[0020] In addition, the parking system 120 or another component of the vehicle 102 can use the sensors 118 to obtain an initial pose 104 and/or a goal pose 106 of the vehicle 102 (e.g., to park in the available space 110). The sensors 118 can also be used to generate an obstacle map for the environment 100 that includes the stationary objects 112 and moving objects 114.))
measure a distance between each of the plurality of location points and each of the plurality of obstacles using the vehicle perception sensor; and (([0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1):
[0073] At step 804, an obstacle map for an environment that includes the initial pose and the goal pose is obtained. For example, the motion planner 122 can obtain the obstacle map from a perception system. The obstacle map can be a radar occupancy grid map or a radar-centric occupancy grid map for the environment 100. The obstacles in the obstacle map can be represented by bounding boxes, circles, occupancy grids, free-space polygons, or any combination thereof. The motion planner 122 may also obtain a reference path.))
calculate the repulsive potential at each of the plurality of location points,
. (([0036] Two general kinds of artificial potential fields are generated within the parking trajectory algorithm: attractive potentials and repulsive potentials. In general, the goal pose and a reference path (if provided) exhibit an attractive potential, while obstacles (e.g., moving objects 114, stationary objects 112, and boundaries) produce repulsive potentials. The attractive or repulsive potentials are a function of the distance (e.g., an inverse, linear, quadratic, or exponential relationship) between the vehicle 102 and the potential source. As a result, the total potential, U.sub.total(s, l, t), at any point or node within the parking environment is determined from the sum of the attractive potentials, U.sub.attractive(s, l, t), and the repulsive potentials, U.sub.repulsive(s, l, t), as illustrated in Equation (1):) ))
does not teach wherein the repulsive potential at each of the plurality of location points is defined by a repulsive potential function:
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wherein Ureps is the repulsive potential function, s is a vector describing a location of one of the plurality of location points, kr is a predetermined repulsive constant, ρi is a distance between the location of the one of the plurality of location points and an ith obstacle of the plurality of obstacles, ρ0,i is a minimum allowed distance between the vehicle and the ith obstacle of the plurality of obstacles, and a summation operator Σ indicates a summation over each of the plurality of obstacles.
Abdelbasit, however, does teach wherein the repulsive potential at each of the plurality of location points is defined by a repulsive potential function:
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81
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wherein Ureps is the repulsive potential function, s is a vector describing a location of one of the plurality of location points, kr is a predetermined repulsive constant, ρi is a distance between the location of the one of the plurality of location points and an ith obstacle of the plurality of obstacles, ρ0,i is a minimum allowed distance between the vehicle and the ith obstacle of the plurality of obstacles, and a summation operator Σ indicates a summation over each of the plurality of obstacles.
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Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify in light of Abdelbasit such that the repulsive field is determined by this equation because in magnetism and other forms of repulsion, the relationship between strength of the field and the distance to the object creating the field is often a square of the distance. This would allow stronger forces near the object, and weaker forces further away, which would encourage the vehicle to maintain a distance.
Allowable Subject Matter
Claims 6, 8-10, 13-18 and 19-20 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.
Dupuis et al (US Pub 11,179,850 B2) relates to force vectors from areas to guide robotic paths.
Xiu et al (US Pub 2023/0303120 A1) relates to using attracting and repulsive forces to guide a vehicle.
Gall et al (US Pub 2021/0146961 A1) relates to using attractive and repulsive forces and fields in order to navigate a vehicle.
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/T.J.G./Examiner, Art Unit 3656
/KHOI H TRAN/Supervisory Patent Examiner, Art Unit 3656