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
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2023-006463, filed on 01/19/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/31/2023 was considered by the examiner.
Claim Objections
Claims 1 and 8 are objected to because of the following informalities:
Claim 1 misspells “points” as “pointes” on line 9.
Claim 8 misspells “from” as “form” on line 5.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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) 1-7, and 11 is/are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Templeton (US 9383753 B1).
Regarding claim 1, Templeton teaches An external environment recognition apparatus (Abstract; Fig. 1) comprising:
an in-vehicle (Fig. 1, vehicle 100) detector (sensor system 104) configured to scan and emit an electromagnetic wave (col 8 lines 50-55 “the laser rangefinder or LIDAR unit 128 can be any sensor configured to sense objects in the environment in which the vehicle 100 is located using lasers. The laser rangefinder/LIDAR unit 128 can include one or more laser sources, a laser scanner, and one or more detectors, among other system components.”) in a first direction and as a second direction intersecting the first direction (col 18 lines 18-23 “a single laser in the LIDAR device (e.g., the LIDAR device 302 discussed in connection with FIGS. 3A-3C) can have a scanning range of approximately 150 meters distance, a thirty degree vertical (“altitude”) field of view, and approximately a thirty degree horizontal (“azimuth”) field of view.”). The horizontal (azimuth) field of view is the first direction and the vertical (altitude) is the second direction, which intersects with the first direction.
to detect an external environment situation (col 18 lines 42-50 “During and/or following the scan of the scanning zone (502), data from the LIDAR sensor is analyzed to generate a three-dimensional (“3-D”) point cloud of positions of detected reflective points defining reflective features in the environment surrounding the vehicle. For example, data from the LIDAR sensor can include correlated lists of orientation of the LIDAR device (e.g., altitude and azimuth angles), to indicate direction to each point, and time delay between emission and reception, to indicate distance to each point”) around a subject vehicle (vehicle 100);
a microprocessor (Fig. 1, computer system 112, processor 113) configured to acquire road surface information of a road (Figs. 4C and 4D) on which the subject vehicle travels based on a detection data of the in-vehicle detector (col 27 lines 1-11 “A baseline point map can be, for example, a three-dimensional map of fixed structures along a roadway that is generated in advance. Such a baseline map can indicate, for example, the approximate three-dimensional locations of curbs, trees, buildings, road signs, permanent barricades, and/or other substantially permanent objects and environmental features defining the surroundings of a roadway or other vehicular path of travel. In some examples, the baseline 3-D map is generated by a LIDAR device actively monitoring a scene while traversing the scene substantially in the absence of transient objects”). The baseline map, acquired by the LIDAR device while traversing, and including road surface information (as shown in Fig. 4D, and indicating such features as curbs), is the road surface information of the road; and
a memory coupled to the microprocessor (Fig. 1, data storage 114), wherein
the in-vehicle detector acquires a three-dimensional point cloud data including distance information for each of a plurality of detection pointes (col 18 lines 42-50 “During and/or following the scan of the scanning zone (502), data from the LIDAR sensor is analyzed to generate a three-dimensional (“3-D”) point cloud of positions of detected reflective points defining reflective features in the environment surrounding the vehicle. For example, data from the LIDAR sensor can include correlated lists of orientation of the LIDAR device (e.g., altitude and azimuth angles), to indicate direction to each point, and time delay between emission and reception, to indicate distance to each point”) in a matrix form (col 26 lines 24-28 “the scanning zone is analogous to an arrangement of points distributed in azimuth and elevation, to form a grid-like structure, with each row having a common altitude orientation, and each column having a common azimuth orientation.”) frame by frame (col 17 lines 47-56 “For each captured point cloud, positions of perceived objects and their corresponding boundary definitions are associated with a frame number or frame time. Thus, similarly shaped objects appearing in roughly similar locations in successive scans of the scene can be associated with one another to track objects in time. For perceived objects appearing in multiple point cloud frames (e.g., complete scans of the scanning zone), the object can be associated, for each frame on which the object appears, with a distinct bounding shape defining the dimensional extent of the perceived object.”), and the microprocessor is configured to perform:
recognizing a surface of the road (Fig. 7A; col 18 lines 42-50 “During and/or following the scan of the scanning zone (502), data from the LIDAR sensor is analyzed to generate a three-dimensional (“3-D”) point cloud of positions of detected reflective points defining reflective features in the environment surrounding the vehicle. For example, data from the LIDAR sensor can include correlated lists of orientation of the LIDAR device (e.g., altitude and azimuth angles), to indicate direction to each point, and time delay between emission and reception, to indicate distance to each point”) and a three-dimensional object on the road (Fig. 7B, spatial data 316) for each frame as the road surface information based on the three-dimensional point cloud data (col 17 lines 52-56 “For perceived objects appearing in multiple point cloud frames (e.g., complete scans of the scanning zone), the object can be associated, for each frame on which the object appears, with a distinct bounding shape defining the dimensional extent of the perceived object.”), and
determining an interval of detection points of the three-dimensional point cloud data of a next frame (Figs. 6C and 9A; col 31 lines 13-19 “the angular separation between successively emitted pulses is given approximately by the product of the angular rate of change of the beam steering optics and the interval between successive pulses (e.g., approximately the product of ω(t) and t.sub.1). Thus, reducing the slew rate of the LIDAR device 302 reduces the angular separation between successively emitted pulses.”) used for recognition in the recognizing as a scanning angular resolution of the electromagnetic wave (Fig. 8, step 506) based on a size of a predetermined three-dimensional object determined in advance as a recognition target and a distance from the subject vehicle to the predetermined three-dimensional object (col 52 line 63 – col 26 5 “The region for enhanced resolution study can therefore be larger than the predicted size of the moving object as determined by the point cloud data. For example, the region for enhanced study can include the predicted location of the moving object during the next scan, and can be enlarged, relative to the size of the moving object, according to the uncertainty of the predicted location. The uncertainty can be formulaic, such as based on the expected size of the moving object, or a standard angular separation surrounding the position of the moving object.”).
Regarding claim 2, Templeton teaches The external environment recognition apparatus according to claim 1, wherein the microprocessor is configured to further perform setting an interval of irradiation points in a horizontal direction and a vertical direction according to the scanning angular resolution of the electromagnetic wave determined in the determining (col 14 lines 50-57 “it is noted that a more complete three-dimensional sampling is provided by either adjusting the beam steering optics 304 to direct the laser beam 306 up or down from the x-y plane on its next sweep of the scene or by providing additional lasers and associated beam steering optics dedicated to sampling point locations in planes above and below the x-y plane shown in FIG. 3B, or combinations of these.”; col 32 lines 35-41 “the techniques described herein to provide enhanced angular resolution LIDAR scans by adjusting pulse rate (FIG. 5A) or by adjusting slew rate (FIG. 8) can be combined together to provide a desired angular resolution. For example, enhanced angular resolution can be achieved by simultaneously increasing pulse rate and decreasing slew rate.”).
Regarding claim 3, Templeton teaches The external environment recognition apparatus according to claim 1, wherein the in-vehicle detector scans and emits the electromagnetic wave in a horizontal direction (“azimuth”) as the first direction and a vertical direction (“altitude”) as the second direction to acquire the three-dimensional point cloud data including the distance information for each of the plurality of detection points arranged in the horizontal direction and the vertical direction frame by frame (col 17 lines 47-56 “For each captured point cloud, positions of perceived objects and their corresponding boundary definitions are associated with a frame number or frame time. Thus, similarly shaped objects appearing in roughly similar locations in successive scans of the scene can be associated with one another to track objects in time. For perceived objects appearing in multiple point cloud frames (e.g., complete scans of the scanning zone), the object can be associated, for each frame on which the object appears, with a distinct bounding shape defining the dimensional extent of the perceived object.”), and
the microprocessor is configured to perform the determining including determining the scanning angular resolution in the vertical direction of the electromagnetic wave corresponding to the interval of the detection points in the vertical direction of the three-dimensional point cloud data of a next frame (Figs. 6C and 9A; col 31 lines 13-19 “the angular separation between successively emitted pulses is given approximately by the product of the angular rate of change of the beam steering optics and the interval between successive pulses (e.g., approximately the product of ω(t) and t.sub.1). Thus, reducing the slew rate of the LIDAR device 302 reduces the angular separation between successively emitted pulses.”) used for the recognition in the recognizing (Fig. 8, step 506) based on a length in the vertical direction of the predetermined three-dimensional object and the distance from the subject vehicle to the predetermined three-dimensional object (col 52 line 63 – col 26 5 “The region for enhanced resolution study can therefore be larger than the predicted size of the moving object as determined by the point cloud data. For example, the region for enhanced study can include the predicted location of the moving object during the next scan, and can be enlarged, relative to the size of the moving object, according to the uncertainty of the predicted location. The uncertainty can be formulaic, such as based on the expected size of the moving object, or a standard angular separation surrounding the position of the moving object.”; col 17 lines 32-38 “The object detector can be pre-loaded (or dynamically instructed) to associate arrangements according to one or more parameters corresponding to physical objects/features in the environment surrounding the vehicle 100. For example, the object detector can be pre-loaded with information indicating a typical height of a pedestrian, a length of a typical automobile, confidence thresholds for classifying suspected objects, etc.”).
Regarding claim 4, Templeton teaches The external environment recognition apparatus according to claim 3, wherein the microprocessor is configured to perform the determining including further determining an interval of the detection points in the vertical direction of the three-dimensional point cloud data of the next frame corresponding to the surface of the road (Fig. 7A; col 18 lines 42-50 “During and/or following the scan of the scanning zone (502), data from the LIDAR sensor is analyzed to generate a three-dimensional (“3-D”) point cloud of positions of detected reflective points defining reflective features in the environment surrounding the vehicle. For example, data from the LIDAR sensor can include correlated lists of orientation of the LIDAR device (e.g., altitude and azimuth angles), to indicate direction to each point, and time delay between emission and reception, to indicate distance to each point”) and the predetermined three-dimensional object on the road (col 24 lines 44-53 “a region of a LIDAR-indicated point cloud can be identified for enhanced angular resolution analysis based on a combination of factors. For example, enhanced angular resolution analysis can be initiated in response to identifying factors in one or more of the point cloud distance map provided by the LIDAR device, the intensity map of reflected light (e.g., from the LIDAR-received reflected light signals), an estimate of the location of the autonomous vehicle and/or pre-mapped objects of interest, an output from additional sensors such as the camera 210, etc.” ) at a required distance (col 28 lines 12-30 “The threshold distance can be set according to a desired spatial resolution to allow object identification/categorization with desired reliability. The spacing between adjacent points in the point cloud is given, at least approximately, by the arc length distance mapped by the angular change in the LIDAR orientation at the line of sight distance of the mapped features. Because arc length scales with radius (line of sight distance), the spatial resolution achieved by the LIDAR device is inversely proportionate to the line of sight distance to reflective features. Thus, distant objects can have relatively low spatial resolution which prevents accurate object identification/categorization. For example, in the case of an approaching car that first appears as a very small object on the horizon, scanning the distant car with enhanced angular resolution, allows the car to be identified and its position and motion to be characterized sooner than otherwise possible, which enables object avoidance and similar functions to be undertaken sooner.”)
based on a vehicle speed of the subject vehicle as a first scanning angular resolution (col 28 lines 12-30 “The threshold distance can be set according to a desired spatial resolution to allow object identification/categorization with desired reliability. The spacing between adjacent points in the point cloud is given, at least approximately, by the arc length distance mapped by the angular change in the LIDAR orientation at the line of sight distance of the mapped features. Because arc length scales with radius (line of sight distance), the spatial resolution achieved by the LIDAR device is inversely proportionate to the line of sight distance to reflective features. Thus, distant objects can have relatively low spatial resolution which prevents accurate object identification/categorization. For example, in the case of an approaching car that first appears as a very small object on the horizon, scanning the distant car with enhanced angular resolution, allows the car to be identified and its position and motion to be characterized sooner than otherwise possible, which enables object avoidance and similar functions to be undertaken sooner.”; col 6 lines 47-54 “the refresh rate may be higher at high rates of speeds, because at high speeds potential obstacles (and the need to maneuver around them) tend to develop on relatively short time scales for a potential obstacle at a fixed distance. On the other hand, the refresh rate may be lower at low rates of speed, because at low speeds potential obstacles (and the need to maneuver around them) tend to develop on relatively greater time scales.”) and
determining an interval of the detection points in the vertical direction of the three-dimensional point cloud data of the next frame (col 14 lines 50-57 “it is noted that a more complete three-dimensional sampling is provided by either adjusting the beam steering optics 304 to direct the laser beam 306 up or down from the x-y plane on its next sweep of the scene or by providing additional lasers and associated beam steering optics dedicated to sampling point locations in planes above and below the x-y plane shown in FIG. 3B, or combinations of these.”; col 32 lines 35-41 “the techniques described herein to provide enhanced angular resolution LIDAR scans by adjusting pulse rate (FIG. 5A) or by adjusting slew rate (FIG. 8) can be combined together to provide a desired angular resolution. For example, enhanced angular resolution can be achieved by simultaneously increasing pulse rate and decreasing slew rate.”) corresponding to the surface of the road and the predetermined three-dimensional object on the road at a position where a distance from the subject vehicle is shorter than the required distance (col 28 lines 12-30 “The threshold distance can be set according to a desired spatial resolution to allow object identification/categorization with desired reliability. The spacing between adjacent points in the point cloud is given, at least approximately, by the arc length distance mapped by the angular change in the LIDAR orientation at the line of sight distance of the mapped features. Because arc length scales with radius (line of sight distance), the spatial resolution achieved by the LIDAR device is inversely proportionate to the line of sight distance to reflective features. Thus, distant objects can have relatively low spatial resolution which prevents accurate object identification/categorization. For example, in the case of an approaching car that first appears as a very small object on the horizon, scanning the distant car with enhanced angular resolution, allows the car to be identified and its position and motion to be characterized sooner than otherwise possible, which enables object avoidance and similar functions to be undertaken sooner.”) as a second scanning angular resolution coarser than the first scanning angular resolution (col 5 lines 56-64 “The spatial resolution of a LIDAR-generated 3-D point map depends on the physical separation between points, which is a function of both the distance to the points and the angular separation between the points, with respect to the LIDAR. For example, smaller angular separation between measured points provides higher spatial resolution for a given distance, and vice versa. Similarly, smaller distances result in higher spatial resolution for a given angular separation, and vice versa.”).
Regarding claim 5, Templeton teaches The external environment recognition apparatus according to claim 4, wherein the microprocessor is configured to perform the determining including further determining an interval of the detection points in the vertical direction of the three-dimensional point cloud data of the next frame corresponding to an upper side of the surface of the road (col 26 line 65 – col 27 line 1 “FIG. 7C is a flowchart of another process for identifying regions of a LIDAR-indicated point cloud to examine with enhanced angular resolution by comparison with a baseline point map.”) at the required distance (“threshold distance”) as a third scanning angular resolution coarser than the first scanning angular resolution (col 27 lines 53-55 “the baseline 3-D map is analyzed to identify initial regions for low-resolution scanning and/or high-resolution scanning”).
Regarding claim 6, Templeton teaches The external environment recognition apparatus according to claim 3, wherein the microprocessor is configured to perform the determining including determining the first scanning angular resolution, the second scanning angular resolution, or the third scanning angular resolution in the vertical direction so that the interval of the detection points in the vertical direction of the three-dimensional point cloud data of the next frame used for the recognition in the recognizing match among the plurality of detection points arranged in the horizontal direction (col 14 lines 61-65 “Even though the individual points are not equally spatially distributed throughout the sampled environment, adjacent sampled points are roughly equally angularly spaced with respect to the LIDAR device 302.”; col 26 lines 24-28 “the scanning zone is analogous to an arrangement of points distributed in azimuth and elevation, to form a grid-like structure, with each row having a common altitude orientation, and each column having a common azimuth orientation.”; col 6 lines 17-24 “For a conventional LIDAR system that does not provide adaptive angular resolution adjustments, and instead provides equally spaced sample points across the scanning zone, the maximum theoretical angular resolution of the system is determined by the refresh rate (number of complete scans to be completed per second), the total solid angle scanned during each complete scan, and the maximum sustained pulse rate.”).
Regarding claim 7, Templeton teaches The external environment recognition apparatus according to claim 3, wherein the microprocessor is configured to perform the determining including determining the scanning angular resolution in the horizontal direction of the electromagnetic wave (Figs. 6C and 9A; col 31 lines 13-19 “the angular separation between successively emitted pulses is given approximately by the product of the angular rate of change of the beam steering optics and the interval between successive pulses (e.g., approximately the product of ω(t) and t.sub.1). Thus, reducing the slew rate of the LIDAR device 302 reduces the angular separation between successively emitted pulses.”) corresponding to the interval of the detection points in the horizontal direction of the three-dimensional point cloud data of the next frame (col 32 lines 35-41 “the techniques described herein to provide enhanced angular resolution LIDAR scans by adjusting pulse rate (FIG. 5A) or by adjusting slew rate (FIG. 8) can be combined together to provide a desired angular resolution. For example, enhanced angular resolution can be achieved by simultaneously increasing pulse rate and decreasing slew rate.”) used for the recognition in the recognizing (Fig. 8, step 506) based on a length in the horizontal direction of the predetermined three-dimensional object and the distance from the subject vehicle to the predetermined three-dimensional object (col 52 line 63 – col 26 5 “The region for enhanced resolution study can therefore be larger than the predicted size of the moving object as determined by the point cloud data. For example, the region for enhanced study can include the predicted location of the moving object during the next scan, and can be enlarged, relative to the size of the moving object, according to the uncertainty of the predicted location. The uncertainty can be formulaic, such as based on the expected size of the moving object, or a standard angular separation surrounding the position of the moving object.”; col 17 lines 32-38 “The object detector can be pre-loaded (or dynamically instructed) to associate arrangements according to one or more parameters corresponding to physical objects/features in the environment surrounding the vehicle 100. For example, the object detector can be pre-loaded with information indicating a typical height of a pedestrian, a length of a typical automobile, confidence thresholds for classifying suspected objects, etc.”).
Regarding claim 11, Templeton teaches The external environment recognition apparatus according to claim 1, wherein the in-vehicle detector is a LiDAR (laser rangefinder/LIDAR unit 128).
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.
Claim(s) 8-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Templeton as applied to claim 1 above, and further in view of Englard et al. (US 20190179025 A1).
Regarding claim 8, Templeton teaches The external environment recognition apparatus according to claim 1, wherein the microprocessor is configured to further perform when a farthest distance of the surface of the road in a traveling direction of the subject vehicle recognized in the recognizing is shorter than the required distance based on the vehicle speed of the subject vehicle (‘maximum distance sensitivity”),
setting an irradiation angle of the electromagnetic wave in the vertical direction such that the surface of (Fig. 7A, steps 714 and 716) and the predetermined three-dimensional object on the road (Fig. 7B, step 724) which is at the required distance away from the subject vehicle (col 28 lines 12-30 “The threshold distance can be set according to a desired spatial resolution to allow object identification/categorization with desired reliability. The spacing between adjacent points in the point cloud is given, at least approximately, by the arc length distance mapped by the angular change in the LIDAR orientation at the line of sight distance of the mapped features. Because arc length scales with radius (line of sight distance), the spatial resolution achieved by the LIDAR device is inversely proportionate to the line of sight distance to reflective features. Thus, distant objects can have relatively low spatial resolution which prevents accurate object identification/categorization. For example, in the case of an approaching car that first appears as a very small object on the horizon, scanning the distant car with enhanced angular resolution, allows the car to be identified and its position and motion to be characterized sooner than otherwise possible, which enables object avoidance and similar functions to be undertaken sooner.”) (col 32 lines 35-41 “the techniques described herein to provide enhanced angular resolution LIDAR scans by adjusting pulse rate (FIG. 5A) or by adjusting slew rate (FIG. 8) can be combined together to provide a desired angular resolution. For example, enhanced angular resolution can be achieved by simultaneously increasing pulse rate and decreasing slew rate.”).
Templeton does not teach the apparatus, comprising: predicting a gradient of the road form the farthest distance to the required distance.
Englard teaches an analogous apparatus (Abstract), predicting ([0056] lines 1-4, “The sensor control architecture 100 also includes a prediction component 120, which processes the perception signals 106 to generate prediction signals 122 descriptive of one or more predicted future states of the vehicle's environment.”) a gradient of the road (Figs. 10-11; [0131] lines 5-11, “In particular, the road configuration (e.g., slope), and possibly the orientation of the vehicle itself (e.g., heading downhill at a certain angle), may be analyzed to ensure that one or more of the sensors 602 are focused so as to collect more useful data, without, for example, being overly focused on the road immediately ahead of the vehicle or overly focused on the sky.”; [0230] lines 3-7, “Block 1026 includes determining a slope of at least one of the identified road portions, and may also include determining other aspects of the configuration, such as the amount that (and/or the manner in which) the road portion(s) turn to the left and/or right.”) form the farthest distance to the required distance (Figs. 10-11; [0233] lines 4-8, “the method 1020 may include a first additional block in which it is determined that, at the elevation determined at block 1028, the sensing distance of the first sensor is less than some threshold distance (e.g., 50 meters, 20 meters, etc.)”). The road configuration (e.g. slope) is the gradient of the road, and the range including the sensing distance to the threshold distance is from the farthest distance to the required distance.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Templeton to include the prediction of the gradient of the road of Englard because it would yield predictable and advantageous results, such as accurately identifying the surface of the road, thereby more accurately determining sensed distances of detected objects.
Regarding claim 9, Templeton in view of Englard teaches The external environment recognition apparatus according to claim 8, wherein the microprocessor is configured to perform the predicting including, when the farthest distance recognized in the recognizing is shorter than the required distance (Englard: [0233] lines 4-8, “the method 1020 may include a first additional block in which it is determined that, at the elevation determined at block 1028, the sensing distance of the first sensor is less than some threshold distance (e.g., 50 meters, 20 meters, etc.)”), predicting a gradient of the road from the farthest distance to the required distance based on a measurement data of a gradient of the road from depth distance (Englard: Figs. 10-11) corresponding to a lower end of FOV of the in-vehicle detector to the farthest distance (Englard: [0231] lines 1-11, “At block 1028, an elevation of the field of regard of the first sensor is determined such that one or more visibility criteria are satisfied, by analyzing at least the configuration determined at block 1026. For example, the elevation may be one that maximizes a sensing distance of the first sensor in a direction along which the vehicle is expected to travel. The sensing distance is generally limited by the range of the first sensor, but may be maximized by choosing an elevation that does not cause the first sensor to “look” too high (e.g., into the sky) or too low (e.g., into the road a relatively short distance in front of the vehicle).”; [0132] lines 7-13, “A sensor direction 704 may represent the center of the field of regard of a sensor (e.g., lidar device, camera, etc.), or the center of a bottom edge of the field of regard, etc. Alternatively, the sensor direction 704 may represent an area of highest focus within the field of regard (e.g., a densest concentration of horizontal scan lines for a lidar or radar device).”).
Regarding claim 10, Templeton in view of Englard teaches The external environment recognition apparatus according to claim 8, wherein the microprocessor is configured to perform the determining including further determining the interval of the detection points of the three-dimensional point cloud data of the next frame (Templeton: “enhanced resolution”) corresponding to the surface of (Templeton: Fig. 7A, step 714) and the predetermined three-dimensional object on the road (Templeton: Fig. 7B step 724) from the farthest depth distance (Templeton: Fig. 7D, threshold distance) to the required distance (Templeton: “maximum distance sensitivity”) whose gradient is predicted in the predicting (Englard: Block 1026) as the scanning angular resolution of the electromagnetic wave based on a size of the predetermined three-dimensional object and the distance form the subject vehicle to the predetermined three-dimensional object (Templeton: col 52 line 63 – col 26 5 “The region for enhanced resolution study can therefore be larger than the predicted size of the moving object as determined by the point cloud data. For example, the region for enhanced study can include the predicted location of the moving object during the next scan, and can be enlarged, relative to the size of the moving object, according to the uncertainty of the predicted location. The uncertainty can be formulaic, such as based on the expected size of the moving object, or a standard angular separation surrounding the position of the moving object.”; col 17 lines 32-38 “The object detector can be pre-loaded (or dynamically instructed) to associate arrangements according to one or more parameters corresponding to physical objects/features in the environment surrounding the vehicle 100. For example, the object detector can be pre-loaded with information indicating a typical height of a pedestrian, a length of a typical automobile, confidence thresholds for classifying suspected objects, etc.”).
Conclusion
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/B.B.G./Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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