DETAILED ACTION
This is the First Office Action on the Merits and is directed towards new claims 21-40 as preliminarily amended and filed on 12/23/2024.
This Application is subject to a double patent rejection with the parent application.
Notice of Pre-AIA or AIA Status
Priority is claimed as set forth below, accordingly the earliest effective filing date is July 28, 2022 (20220728).
The present application, effectively filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
This application is a continuation application of U.S. application no. 17/876,340, filed July 28, 2022 (20220728) now U.S. Patent 12,211,288 (“Parent Application”). See MPEP §201.07[R-08.2017]. In accordance with MPEP §609.02 [R-07.2015] Section A. 2 and MPEP §2001.06(b)[R-08.2017] (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application. Also in accordance with MPEP §2001.06(b) [R-08.2017] (last paragraph), all documents cited or considered ‘of record’ in the Parent Application are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicants desire the information to be printed on a patent issuing from this application. See MPEP §609.02 [R-07.2015] Section A. 2. Finally, Applicants are reminded that the prosecution history of the Parent Application is relevant in this application. See e.g., Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823 (Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents).
Specification
The disclosure is objected to because of the following informalities: paragraphs [0001] should be updated to reflect the issuance of the parent application identified above.
Appropriate correction is required.
Information Disclosure Statement
As required by M.P.E.P. 609 [R-07.2022], Applicant's 01/13/2025 and 02/07/2025 submission(s) of Information Disclosure Statement (IDS)(s) is/are acknowledged by the Examiner and the reference(s) cited therein has/have been considered in the examination of the claim(s) now pending. A copy of the submitted IDS(s) initialed and dated by the Examiner is/are attached to the instant Office action.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 21-23, 28-34, and 37-40 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220076036 A1 to TAIEB; Yoav et al. (hereinafter Taieb cited in the 01/13/2025 IDS) in view of US 20200238996 A1 to Pendleton; Scott D. et al. (hereinafter Pendleton cited in the 01/13/2025 IDS) and in further view of US 20190332875 A1 to Vallespi-Gonzalez; Carlos et al. (hereinafter Vallespi).
Regarding claim 21 Taieb teaches in for example the Figure(s) reproduced immediately below:
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and associated descriptive texts a method, comprising:
deriving, with at least one processor, a first state of a traffic light at an intersection a vehicle is approaching (given the Broadest Reasonable Interpretation (BRI) is shown in the figures above and especially step 4403 of Fig. 44 wherein it is understood the intersection is the one shown in Fig. 43A and the vehicle is the host vehicle 4202 in Fig. 42 as explained in for example only paras:
“[0564] FIGS. 43A and 43B illustrate exemplary images a vehicle's surrounding environment consistent with the disclosed embodiments. Camera 4203A of host vehicle 4202 may capture a first image 4301 representing the environment of vehicle 4202 and transmit first image 4301 to the navigation system (e.g., to a processor of vehicle 4202). First image 4301 may include a representation of traffic light 4311, which is positioned directly in front of host vehicle 4202 along lane 4321. First image 4301 may also include a representation of traffic light 4312, which is not positioned directly in front of vehicle 4202. For example, traffic light 4312 may face a direction along lane 4322, which may be perpendicular to lane 4321 on which vehicle 4202 is traveling.
[0575] At step 4403, the first image may be analyzed to generate a first detection result. In some embodiments, the first detection result may include an identification of a traffic light and a state of the traffic light. For example, the processor of vehicle 4202 may be configured to analyze first image 4301. The processor may also be configured to identify traffic light 4311 and the state of traffic light 4311 (e.g., having a red light state). The processor may further be configured to generate a first detection result including the identification of traffic light 4311 and a state of traffic light 4311.”),
according to first detection data acquired by a first traffic light detection (TLD) system (see step 4401 of Fig. 44 as described in para:
“[0570] At step 4401, a first image captured from an environment of the vehicle may be received from a first camera of the vehicle. For example, camera 4203A may capture first image 4301 representing the environment of vehicle 4202. Camera 4203A may transmit first image 4301 to at least one processor of vehicle 4202. In some embodiments, first image 4301 may include representation of one or more traffic lights in the field of view of camera 4203A.”),
the first state connotes;
deriving, with the at least one processor, a second state of the traffic light at the intersection (see step 4404 of Fig. 44, as described in para:
“[0579] At step 4404, the second image may be analyzed to generate a second detection result. The second detection result may include an identification of the traffic light and a state of the traffic light. For example, the processor of vehicle 4202 may be configured to analyze second image 4302. The processor may also be configured to identify traffic light 4311 and a state of traffic light 4311 (e.g., having a red light state) based on the analysis of second image 4302. The processor may further be configured to generate a second detection result including the identification of traffic light 4311 and the state of traffic light 4311 based on the analysis of second image 4302.”),
according to second detection data acquired by a second TLD system that is independent from the first TLD system (see step 4402 of Fig. 44 and para:
“[0571] At step 4402, a second image captured from the environment of the vehicle may be received from a second camera of the vehicle. For example, camera 4203B may capture camera 4203B of the environment of vehicle 4202. Camera 4203B may transmit second image 4302 to the processor of vehicle 4202. In some embodiments, second image 4302 may include representation of one or more traffic lights in the field of view of camera 4203B.”);
determining, with the at least one processor, traffic light information at the intersection based on at least one of:
(i) the first state (see step 4405 in Fig. 44 as described in para:
“[0583] At step 4405, the first detection result and the second detection result may be compared to determine a third detection result. The third detection result may include a confirmed state of the traffic light. For example, the processor of vehicle 4202 may compare the state of the traffic light included in the first detection result and the state of traffic light included in the second detection result. If the state of the traffic light included in the first detection result is consistent with the state of the traffic light included in the second detection result (e.g., both are green light), the processor may determine a third detection result including a confirmed state the traffic light (e.g., green light).”), or
(ii) a result of checking whether the first state is same as the second state (see step 4405 in Fig. 44 as described in para [0583] above);
and causing, with the at least one processor, the vehicle to operate in accordance with the determined traffic light information at the intersection (in steps 4406 and 4407 in fig. 44 as described in paras:
“[0588] At step 4406, a navigational action for the vehicle may be based on the confirmed state of the traffic light. For example, the processor may determine that the confirmed state of the traffic light in front of the host vehicle is red light. The processor may also be configured to determine a navigational action, which may include braking of the host vehicle and stop before the intersection associated with the traffic light. As another example, if the confirmed state of the traffic light in front of the host vehicle is green light, the processor may be configured to a navigational action of maintaining the current heading direction. A navigational action may include one or more of maintaining the current heading direction, steering, braking, or acceleration of the vehicle, or the like, or a combination thereof.
[0592] At step 4407, the vehicle may be caused to implement the navigational action. For example, the processor may transmit a control signal to one or more components of the vehicle, such as a steering mechanism, braking mechanism, or various other components of the vehicle to implement the navigational action. For example, if the processor determines that the confirmed state of the traffic light is red light, the processor may transmit a control signal to the braking mechanism and power mechanism to deaccelerate and stop before an intersection associated with the traffic light.”).
Although the claims are interpreted in light of the specification, limitations from the specification are NOT imported into the claims. The Examiner must give the claim language the Broadest Reasonable Interpretation (BRI) the claims allow.
See MPEP 2111.01 Plain Meaning [R-10.2024], which states
II. IT IS IMPROPER TO IMPORT CLAIM LIMITATIONS FROM THE SPECIFICATION
"Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim. For example, a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment." Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875, 69 USPQ2d 1865, 1868 (Fed. Cir. 2004). See also Liebel-Flarsheim Co. v. Medrad Inc., 358 F.3d 898, 906, 69 USPQ2d 1801, 1807 (Fed. Cir. 2004) (discussing recent cases wherein the court expressly rejected the contention that if a patent describes only a single embodiment, the claims of the patent must be construed as being limited to that embodiment); E-Pass Techs., Inc. v. 3Com Corp., 343 F.3d 1364, 1369, 67 USPQ2d 1947, 1950 (Fed. Cir. 2003) ("Inter US-20100280751-A1 1pretation of descriptive statements in a patent’s written description is a difficult task, as an inherent tension exists as to whether a statement is a clear lexicographic definition or a description of a preferred embodiment. The problem is to interpret claims ‘in view of the specification’ without unnecessarily importing limitations from the specification into the claims."); Altiris Inc. v. Symantec Corp., 318 F.3d 1363, 1371, 65 USPQ2d 1865, 1869-70 (Fed. Cir. 2003) (Although the specification discussed only a single embodiment, the court held that it was improper to read a specific order of steps into method claims where, as a matter of logic or grammar, the language of the method claims did not impose a specific order on the performance of the method steps, and the specification did not directly or implicitly require a particular order). See also subsection IV., below. When an element is claimed using language falling under the scope of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, 6th paragraph (often broadly referred to as means- (or step-) plus- function language), the specification must be consulted to determine the structure, material, or acts corresponding to the function recited in the claim, and the claimed element is construed as limited to the corresponding structure, material, or acts described in the specification and equivalents thereof. In re Donaldson, 16 F.3d 1189, 29 USPQ2d 1845 (Fed. Cir. 1994) (see MPEP § 2181- MPEP § 2186).
In Zletz, supra, the examiner and the Board had interpreted claims reading "normally solid polypropylene" and "normally solid polypropylene having a crystalline polypropylene content" as being limited to "normally solid linear high homopolymers of propylene which have a crystalline polypropylene content." The court ruled that limitations, not present in the claims, were improperly imported from the specification. See also In re Marosi, 710 F.2d 799, 802, 218 USPQ 289, 292 (Fed. Cir. 1983) ("'[C]laims are not to be read in a vacuum, and limitations therein are to be interpreted in light of the specification in giving them their ‘broadest reasonable interpretation.'" (quoting In re Okuzawa, 537 F.2d 545, 548, 190 USPQ 464, 466 (CCPA 1976)). The court looked to the specification to construe "essentially free of alkali metal" as including unavoidable levels of impurities but no more.).”
Taieb does not appear to expressly disclose
wherein deriving the second state of the traffic light at the intersection according to the second detection data comprises:
inferring cross traffic information according to the second detection data,
and deriving the second state of the traffic light based on the inferred cross traffic information,
and wherein the cross traffic information comprises at least one of:
whether a field of view of a cross traffic is occluded,
the cross traffic comprising one or more other vehicles at a cross road segment different from a road segment toward which the vehicle is approaching,
whether the cross traffic is stopped,
whether a distance between the cross traffic and the intersection is greater than a predetermined limit,
or whether a speed of the cross traffic is decreasing.
In analogous art Pendleton teaches in for example, the figures below:
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And associated descriptive texts wherein deriving the second state of the traffic light at the intersection according to the second detection data comprises:
inferring cross traffic information according to the second detection data,
and deriving the second state of the traffic light based on the inferred cross traffic information (in for example para:
“[0059] In an embodiment, the AV system 120 includes communications devices 140 for communicating measured or inferred properties of other vehicles' states and conditions, such as positions, linear and angular velocities, linear and angular accelerations, and linear and angular headings to the AV 100. These devices include Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication devices and devices for wireless communications over point-to-point or ad hoc networks or both. In an embodiment, the communications devices 140 communicate across the electromagnetic spectrum (including radio and optical communications) or other media (e.g., air and acoustic media). A combination of Vehicle-to-Vehicle (V2V) Vehicle-to-Infrastructure (V2I) communication (and, in some embodiments, one or more other types of communication) is sometimes referred to as Vehicle-to-Everything (V2X) communication. V2X communication typically conforms to one or more communications standards for communication with, between, and among autonomous vehicles.”),
and wherein the cross traffic information comprises at least one of:
the cross traffic comprising one or more other vehicles at a cross road segment different from a road segment toward which the vehicle is approaching (see Fig. 13 and para:
“[0118] In one example, the AV 100 is traveling on the road block 1302 and its trajectory continues through the intersection 1300 to road block 1308. Based on information sensed by the range sensors, the planning module 402 determines that the vehicle 1328 traveling on the road block 1306 is in a stationary state. In this example, the information sensed by the range sensor indicates that no other object is in the vicinity of the AV 100 at the intersection 1300, that is, the vehicle 1328 is the only object at the intersection 1300 other than the AV 100. Because the vehicle 1328 remains in a stationary state at the intersection 1300, the planning module 402 determines, that is, infers or estimates, that the traffic light 1320a is green and the traffic light 1320b is red permitting the AV 100 to travel through and requiring that the vehicle 1328 stop at the intersection 1300. Based on this determination of the state of the traffic signal 1318, the planning module 402 operates the AV 100 to travel through the intersection 1300 to road block 1308.”),
whether the cross traffic is stopped (see fig. 13 and para:
“[0119] In another example, the AV 100 is on the road block 1302 in a stationary state at the intersection 1300 and its trajectory continues through the intersection 1300 to road block 1308. Based on information sensed by the range sensors, the planning module 402 determines that the vehicle 1328 traveling on the road block 1306 is in a decelerating state as the vehicle 1328 approaches the intersection 1300. In this example, the information sensed by the range sensor indicates that no other object is in the vicinity of the AV 100 at the intersection 1300, that is, the vehicle 1328 is the only object at the intersection 1300 other than the AV 100. Because the vehicle 1328 is in a decelerating state, the planning module 402 determines, that is, infer or estimate, that the traffic light 1320b is either red or about to turn red, and the traffic light 1320a is green or about to turn green permitting the AV 100 to travel through the intersection 1300. Based on this determination of the state of the traffic signal 1318, the planning module 402 operates the AV 100 to initiate travel through the intersection 1300 to road block 1308.”),
whether a distance between the cross traffic and the intersection is greater than a predetermined limit (see Fig. 13, vehicle 1324 and para:
“[0121] In some embodiments, the movement state of the object includes a direction of travel of the object. In another example, the AV 100 is on the road block 1302 in a stationary state at the intersection 1300 and its trajectory turns left at the intersection 1300 to road block 1312. Based on information sensed by the range sensors, the planning module 402 determines that the vehicle 1322 is either in a stationary state or a decelerating state on the road block 1310 and that the vehicle 1324 traveling on the road block 1306 is turning right at the intersection 1300 to road block 1316. In this example, the information sensed by the range sensor indicates that no other object is in the vicinity of the AV 100 at the intersection 1300, that is, the vehicles 1322 and 1326 are the only objects at the intersection 1300 other than the AV 100. Because the vehicle 1322 is in the stationary state or in the decelerating state and the vehicle 1326 is turning right, the planning module 402 determines, that is, infers or estimates, that the traffic light 1320a is green permitting a left turn from road block 1302 to road block 1312 and that the traffic light 1320c is red. The planning module 402 can additionally determine that the right turn executed by the vehicle 1324 does not affect the left turn to be executed by the AV 100. Based on this determination of the state of the traffic signal 1318 and the movement state of the vehicle 1324, the planning module 402 operates the AV 100 to initiate the left turn from road block 1302 to road block 1312 through the intersection 1300.”),
or whether a speed of the cross traffic is decreasing (see fig. 13 and para:
“[0114] In some embodiments, the planning module 404 can determine the state of the traffic signal 1318 by estimating the state of traversal through the intersection 1300. The state of traversal through the intersection 1300 represents a movement state of objects (for example, vehicle 1322 traveling on road block 1310, vehicle 1324 traveling on road block 1314, pedestrian 1326 attempting to cross road blocks 1302 and 1316) at or approaching the intersection 1300. The movement state includes a stationary state in which an object is stationary, that is, not moving, on a road block or through the intersection. The movement state includes a mobile state in which a vehicle is traveling either toward or away from the traffic signal 1318. The mobile state can additionally include a steady velocity state travel in which a velocity of the vehicle is substantially constant or an accelerating state in which a velocity of the vehicle is increasing over time or a decelerating state in which a velocity of the vehicle is decreasing over time. By determining the state of the traffic signal 1318, that is, the movement state of objects in the vicinity of the AV 100, the planning module 404 determines the state of the traffic signal 1318, in some embodiments, without image data generated from the output of the vision-based sensors such as the camera system 502c or the TLD system 102d or both. In some embodiments, the planning module 404 uses the output of the range sensors, for example, the LiDAR system 502a, the RADAR system 502b, auditory sensors like array microphones, or combinations of them to estimate the state of the intersection 1300.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings disclosed in Pendleton with the vehicle taught in Taieb with a reasonable expectation of success because it would have improved the operation and safety of the autonomous vehicle as taught by Pendleton para:
”[0053] Autonomous vehicles have advantages over vehicles that require a human driver. One advantage is safety. For example, in 2016, the United States experienced 6 million automobile accidents, 2.4 million injuries, 40,000 fatalities, and 13 million vehicles in crashes, estimated at a societal cost of $910+billion. U.S. traffic fatalities per 100 million miles traveled have been reduced from about six to about one from 1965 to 2015, in part due to additional safety measures deployed in vehicles. For example, an additional half second of warning that a crash is about to occur is believed to mitigate 60% of front-to-rear crashes. However, passive safety features (e.g., seat belts, airbags) have likely reached their limit in improving this number. Thus, active safety measures, such as automated control of a vehicle, are the likely next step in improving these statistics. Because human drivers are believed to be responsible for a critical pre-crash event in 95% of crashes, automated driving systems are likely to achieve better safety outcomes, e.g., by reliably recognizing and avoiding critical situations better than humans; making better decisions, obeying traffic laws, and predicting future events better than humans; and reliably controlling a vehicle better than a human.”.
The combination of Taieb above does not appear to expressly disclose wherein the cross traffic information comprises at least one of:
whether a field of view of a cross traffic is occluded.
In analogous art Vallespi teaches wherein the cross traffic information comprises at least one of:
whether a field of view of a cross traffic is occluded, (in for example paras:
“[0052] In some embodiments, generating, based at least in part on the one or more states of the one or more traffic signals, the traffic signal state data comprising a determinative state of the one or more traffic signals can include determining that the determinative state of the one or more traffic signals is the red state when a majority of the one or more regions of interest is at least partly occluded (e.g., blocked or obstructed). For example, the vehicle computing system can determine when the one or more traffic signals include three traffic signals, two of which are occluded by tree branches, that the state of the one or more traffic signals is the red state.
[0171] At 904, the method 900 can include determining that the determinative state of the one or more traffic signals is the red state when a majority of the one or more regions of interest is at least partly occluded. For example, when a group of sections of a traffic signal are blocked by a tree branch the vehicle computing system 112 can determine that the determinative state of the traffic signal is the red state.
[0172] In some embodiments, generating, the traffic signal state data including a determinative state of the one or more traffic signals (e.g., the determinative state of the one or more traffic signals in the method 600) can include determining that the determinative state of the one or more traffic signals is the red state when a majority of the one or more regions of interest is at least partly occluded.”)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings disclosed in Vallespi with the combination of Taieb with a reasonable expectation of success because it would have “been safer” as taught by Vallespi Para(s):
“[0021] For example, a computing system associated with a vehicle can receive data including sensor data associated with one or more states (e.g., physical dimensions and/or location) of an environment; map data including the locations of traffic signals (e.g., geographic location and/or position relative to the vehicle of the traffic signals). Using the sensor data, the map data, and machine-learned model (e.g., a convolutional neural network trained to detect traffic signals), the computing system can detect the traffic signals and determine the state of the traffic signals that were detected. The computing system can then generate traffic signal state data that includes an indication of the state of the traffic signals (e.g., an indication of the color and shape of a traffic signal). This traffic signal state data can be sent to various vehicle systems to perform operations including stopping the vehicle when a traffic signal indicates the vehicle should stop. Accordingly, the disclosed technology allows for safer and more effective vehicle operation through more rapid, accurate, and precise determination of the state of a traffic signal. Further, through more efficient detection of traffic signals and determination of traffic signal states, the disclosed technology can more efficiently utilize available computational resources.”.
Regarding claim 22 and the limitation the method of claim 21, further comprising: determining whether the first state is a specified state (given the BRI, a specified state connotes a red light state described in Taieb para [0575] above).
Regarding claim 23 and the limitation the method of claim 22, comprising:
in response to determining that the first state is different from the specified state,
determining the traffic light information at the intersection based on the first state (given the BRI, a specified state connotes a red light state described in Taieb para [0575-583] above).
Regarding claim 28 and the limitation the method of claim 21, wherein the first TLD system comprises at least one front view camera, and wherein the second TLD system comprises at least one of at least one side view camera, at least one LiDAR sensor, or at least one Radar sensor (see the teachings of Taieb para [0165] and Pendleton para [0063] and the obviousness to combine and the rejection of corresponding parts of claim 21 above incorporated herein by reference
“[0165] In some embodiments, system 100 may use two image capture devices (e.g., image capture devices 122 and 124) in providing navigation assistance for vehicle 200 and use a third image capture device (e.g., image capture device 126) to provide redundancy and validate the analysis of data received from the other two image capture devices. For example, in such a configuration, image capture devices 122 and 124 may provide images for stereo analysis by system 100 for navigating vehicle 200, while image capture device 126 may provide images for monocular analysis by system 100 to provide redundancy and validation of information obtained based on images captured from image capture device 122 and/or image capture device 124. That is, image capture device 126 (and a corresponding processing device) may be considered to provide a redundant sub-system for providing a check on the analysis derived from image capture devices 122 and 124 (e.g., to provide an automatic emergency braking (AEB) system). Furthermore, in some embodiments, redundancy and validation of received data may be supplemented based on information received from one more sensors (e.g., radar, lidar, acoustic sensors, information received from one or more transceivers outside of a vehicle, etc.).
[0063] In an embodiment, the sensors 121 also include sensors for sensing or measuring properties of the AV's environment. For example, monocular or stereo video cameras 122 in the visible light, infrared or thermal (or both) spectra, LiDAR 123, RADAR, ultrasonic sensors, time-of-flight (TOF) depth sensors, speed sensors, temperature sensors, humidity sensors, and precipitation sensors.”).
Regarding claim 29 and the limitation the method of claim 28, wherein the first detection data comprises at least one front view image of the intersection,
and wherein deriving the first state of the traffic light at the intersection according to the first detection data comprises:
deriving the first state of the traffic light according to the at least one front view image of the intersection using at least one of an image processing algorithm,
a sensor tracking algorithm,
or a machine learning model (see the obviousness to combine and the rejection of corresponding parts of claims 28 and 21 above incorporated herein by reference and especially Taieb para:
“[0576] In some embodiments, the processor may identify one or more traffic lights (and a state thereof) from the first image (and/or the second image) based on one or more techniques for identifying a traffic light (and a state thereof) as described elsewhere in this disclosure. For example, the processor determine first candidate objects appearing at locations in the image likely to contain a traffic light and filter the first candidate objects to obtain second candidate objects by excluding those objects unlikely to correspond to a traffic light. The filtering may be done based on various properties associated with traffic lights, such as shape, dimensions, texture, position (e.g., relative to vehicle 200), and the like. Alternatively or additionally, the processor may compare (second) candidate objects with images of various traffic lights (e.g., images of traffic lights having a green light, red light, yellow light, traffic light fixtures, etc.) stored in a memory of the navigation system to determine one or more traffic lights (and a state thereof). As another example, the processor may identify one or more traffic lights (and a state thereof) from the first image (and/or the second image) using a machine learning algorithm. For example, a machine learning algorithm may be applied to model high-level abstractions in one or more portions of an image, and identify one or more traffic lights based on these high-level abstractions.”
And Pendleton para;
[0115] In an embodiment, the state of the traffic signal 1318 is determined by utilizing trained models for the intersection 1300. The trained models are generated by applying machine learning techniques to historical state of traversal data at intersection 1300 or other similar intersections. The trained model takes into account time of day, traffic conditions, pedestrian density based on audiovisual or other data, and weather, among other factors. For example, historical traversal data for the intersection 1300 is gathered and stored. The techniques described here to determine the state of the traffic signal at the intersection 1300 can be applied to the historical traversal data. In some embodiments, the state of the traffic signal is determined at multiple levels of temporal granularity. That is, the state of the traffic signal at each second, each minute, each hour, each day can be determined. Machine learning techniques can be implemented to train the planning module 404 to infer or estimate the state of the traffic signal at the intersection 1300 at a given time instant using the states of the traffic signal determined from the historical traversal data at the multiple levels of temporal granularity. The inferences obtained by machine learning can be improved with the inferences made using information by the range sensors. In addition, the historical traversal data can be updated to improve the machine learning techniques.”).
Regarding claim 30 and the limitation the method of claim 21, wherein the second TLD system comprises at least one of at least one side view camera, at least one LiDAR sensor, or at least one Radar sensor, and wherein inferring cross traffic information according to the second detection data comprises at least one of: inferring whether the field of view of the cross traffic is occluded based on at least one of detection data of the at least one side view camera or detection data of the at least one LiDAR sensor, inferring whether the cross traffic is stopped based on at least one of the detection data of the at least one side view camera, the detection data of the at least one LiDAR sensor, or detection data of the at least one Radar sensor, inferring whether the cross traffic is approaching the intersection based on at least one of the detection data of the at least one LiDAR sensor or the detection data of the at least one Radar sensor, or inferring whether the cross traffic is slowing down based on at least one of detection data of the at least one LiDAR sensor or the at least one Radar sensor (see the obviousness to combine and the rejection of corresponding parts of claims 21 and 28 above incorporated herein by reference).
Regarding claim 32 and the limitation the method of claim 21, wherein deriving the second state of the traffic light based on the inferred cross traffic information comprises:
based on determining that the field of view of the cross traffic is non-occluded,
determining at least one of whether a speed of the cross traffic is no more than zero,
whether a distance between the cross traffic and the intersection is greater than a predetermined limit,
or whether the speed of the cross traffic is decreasing (see the rejection of corresponding parts of claims 21 and especially the teachings of Pendleton above incorporated herein by reference).
Regarding claim 33 and the limitation the method of claim 32, wherein deriving the second state of the traffic light based on the inferred cross traffic information comprises at least one of:
based on determining that the speed of the cross traffic is no more than zero, determining the second state of the traffic light to be green,
based on determining that the speed of the cross traffic is no more than zero, determining whether the distance between the cross traffic and the intersection is greater than the predetermined limit,
based on determining that the distance is greater than the predetermined limit, determining the second state of the traffic light to be green,
based on determining that the distance is smaller than or identical to the predetermined limit, determining whether the speed of the cross traffic is decreasing,
based on determining that the speed of the cross traffic is decreasing, determining the second state of the traffic light to be green, or
based on determining that the speed of the cross traffic is increasing, determining the second state of the traffic light to be red (see the obviousness to combine and the rejection of corresponding parts of claims 21 and especially the teachings of Pendleton above incorporated herein by reference).
Regarding claim 34 and the limitation the method of claim 21, wherein the intersection is associated with a plurality of road segments comprising: a first path segment towards which the vehicle is approaching, the traffic light controlling vehicular movement at the first path segment, and at least one cross road segment adjacent to the first path segment, a cross traffic light controlling a corresponding cross traffic movement at each of the at least one cross road segment, the cross traffic light being coordinated with the traffic light for the first path segment (see the obviousness to combine and the rejection of corresponding parts of claim 21 above and especially the teachings of Pendleton Fig. 13 above incorporated herein by reference).
Regarding claim 31 and the limitation the method of claim 21, wherein deriving the second state of the traffic light based on the inferred cross traffic information comprises:
based on determining that the field of view of the cross traffic is occluded, determining the second state of the traffic light to be red (see the rejection of corresponding parts of claim 21 above incorporated herein by reference and especially the teachings of Vallespi).
Regarding claim 37 and the limitation the method of claim 21, further comprising:
in response to determining that a distance from the vehicle to the intersection satisfies a predetermined threshold, initiating, with the at least one processor, at least one of the first TLD system or the second TLD system to detect the traffic light at the intersection.
Regarding claim 38 and the limitation A system, comprising: at least one processor, and at least one non-transitory storage media storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising: deriving a first state of a traffic light at an intersection a vehicle is approaching, according to first detection data acquired by a first traffic light detection (TLD) system; deriving a second state of the traffic light at the intersection, according to second detection data acquired by a second TLD system that is independent from the first TLD system; determining traffic light information at the intersection based on at least one of (i) the first state or (ii) a result of checking whether the first state is same as the second state; and causing the vehicle to operate in accordance with the determined traffic light information at the intersection, wherein deriving the second state of the traffic light at the intersection according to the second detection data comprises: inferring cross traffic information according to the second detection data, and deriving the second state of the traffic light based on the inferred cross traffic information, and wherein the cross traffic information comprises at least one of: whether a field of view of a cross traffic is occluded, the cross traffic comprising one or more other vehicles at a cross road segment different from a road segment toward which the vehicle is approaching, whether the cross traffic is stopped, whether a distance between the cross traffic and the intersection is greater than a predetermined limit, or whether a speed of the cross traffic is decreasing (see the obviousness to combine and the rejection of corresponding parts of claim 21 above incorporated herein by reference).
Regarding claim 39 and the limitation the system of claim 38, wherein deriving the second state of the traffic light based on the inferred cross traffic information comprises: based on determining that the field of view of the cross traffic is occluded, determining the second state of the traffic light to be red, or based on determining that the field of view of the cross traffic is non-occluded, determining at least one of whether a speed of the cross traffic is no more than zero, whether a distance between the cross traffic and the intersection is greater than a predetermined limit, or whether the speed of the cross traffic is decreasing (see the obviousness to combine and the rejection of corresponding parts of claims 38 and 21 above incorporated herein by reference).
Regarding claim 40 and the limitation at least one non-transitory storage media storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
deriving a first state of a traffic light at an intersection a vehicle is approaching, according to first detection data acquired by a first traffic light detection (TLD) system;
deriving a second state of the traffic light at the intersection, according to second detection data acquired by a second TLD system that is independent from the first TLD system;
determining traffic light information at the intersection based on at least one of
(i) the first state or
(ii) a result of checking whether the first state is same as the second state;
and causing the vehicle to operate in accordance with the determined traffic light information at the intersection,
wherein deriving the second state of the traffic light at the intersection according to the second detection data comprises:
inferring cross traffic information according to the second detection data, and deriving the second state of the traffic light based on the inferred cross traffic information,
and wherein the cross traffic information comprises at least one of:
whether a field of view of a cross traffic is occluded,
the cross traffic comprising one or more other vehicles at a cross road segment different from a road segment toward which the vehicle is approaching,
whether the cross traffic is stopped,
whether a distance between the cross traffic and the intersection is greater than a predetermined limit, or whether a speed of the cross traffic is decreasing (see the obviousness to combine and the rejection of corresponding parts of claim 21 above incorporated herein by reference).
Claims 24-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220076036 A1 to TAIEB; Yoav et al. (hereinafter Taieb cited in the 01/13/2025 IDS) in view of US 20200238996 A1 to Pendleton; Scott D. et al. (hereinafter Pendleton cited in the 01/13/2025 IDS) and in further view of US 20190332875 A1 to Vallespi-Gonzalez; Carlos et al. (hereinafter Vallespi) as applied to the claims above in view of US 20210261152 A1 to MEIJBURG; Maria Antoinette et al. (hereinafter MEIJBURG cited in the 01/13/2025 IDS).
Regarding claim 24 the combination of Taieb does not appear to expressly disclose however in analogous art MEIJBURG teaches in for example the figures below:
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And associated descriptive texts the limitations a method comprising: in response to determining that the first state is the specified state, checking whether the first state is same as the second state; and determining the traffic light information at the intersection based on the result of the checking (in para:
“[0125] The perception module 1300 uses N-modular redundancy to detect the traffic signal 1310. In some embodiments, N is 3. In other embodiments, N can be 4, 5, 6, etc., depending on the number of cameras. For example, to detect the traffic signal 1310, the circuits 1324a, 1320 detect that the traffic signal 1310 is a first color (for example, green) based on the digital video stream 1308a from the camera 1306a. The circuits 1324b, 1320 detect that the traffic signal 1310 is a second color (for example, red) based on the digital video stream 1308b from the camera 1306b. The circuits 1324c, 1320 detect that the traffic signal 1310 is a third color (for example, red) based on the DSRC message 1318 received by the DSRC sensor 1316 of the AV 100. Responsive to detecting that the third color (red) is the same as the second color, the circuit 1320 associates the traffic signal 1310 with the second color (red).”) .
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method disclosed in MEIJBURG with the method taught in the combination of Taieb with a reasonable expectation of success because it would have “increased the safety of the autonomous vehicle” as taught by MEIJBURG Para(s):
“[0126] When the camera 1306a is non-operational, there is latency in the digital video stream 1308a, or the traffic signal detection fails for another reason, such as a mismatch between the digital video streams 1308a, 1308b, the circuit 1320 detects a failure to detect the traffic signal 1310 of the traffic light 1404. Responsive to detecting the failure, the circuit 1320 associates the traffic signal 1310 with a red light. Thus, for safety, the perception module 1300 sets the a traffic light state to a red light as a default worst-case scenario whenever the circuit 1320 is unable to detect the traffic signal 1310.”.
Regarding claim 25 and the limitation the method of claim 24, wherein determining the traffic light information at the intersection based on the result of checking comprises: in response to determining that the specified state is same as the second state, determining that a current state of the traffic light at the intersection is the specified state (see Taieb para [0579] above).
Regarding claim 26 and the limitation the method of claim 24, wherein determining the traffic light information at the intersection based on the result of checking comprises: in response to determining that the specified state is different from the second state, determining that a current state of the traffic light at the intersection is a red state (see obviousness to combine and the rejection of corresponding parts of claims 24 and 21 above incorporated herein by reference and especially MEIJBURG Para(s) [0126] “Thus, for safety, the perception module 1300 sets the a traffic light state to a red light as a default worst-case scenario whenever the circuit 1320 is unable to detect the traffic signal 1310.”).
Claim 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220076036 A1 to TAIEB; Yoav et al. (hereinafter Taieb cited in the 01/13/2025 IDS) in view of US 20200238996 A1 to Pendleton; Scott D. et al. (hereinafter Pendleton) and in further view of US 20190332875 A1 to Vallespi-Gonzalez; Carlos et al. (hereinafter Vallespi) as applied to the claims above in view of US 20210261152 A1 to MEIJBURG; Maria Antoinette et al. (hereinafter MEIJBURG) as applied to the claims above in view of US 20230368673 A1 to Rusciano; Domenico et al. (hereinafter Rusciano).
Regarding claim 27 the combination of Taieb does not appear to expressly disclose however n analogous art Rusciano teaches the limitation the method further comprising:
in response to determining that a duration of determining that the specified state is different from the second state is more than a predetermined time duration, initiating, with the at least one processor, an external support (in for example para:
“[0037] At step 204, the first vehicle logs the time, its location, and its direction of travel. In some examples, the first vehicle continuously (or periodically) logs information, even before the vehicle is stranded. Thus, step 204 can include retrieving information from a log buffer that was logged before the first vehicle became stranded. The first vehicle can log other information as well, such as the number of passengers in the first vehicle, any sensed damage to the first vehicle, and whether airbags deployed in the first vehicle. In some examples, the first vehicle logs this information at the time of an incident that causes the vehicle to become stranded and in need of recovery. In some examples, the first vehicle periodically updates this information, such as every second, every five seconds, or any time there is any change to the vehicle (e.g., the vehicle is moved, bumped, etc.). In some examples, a stranded vehicle may shut down and not be able to log information after an incident that caused the shutdown.
[0064] In some examples, autonomous vehicles in an autonomous vehicle fleet can provide some of the above services to non-autonomous vehicles or to vehicles in other fleets. For example, if an autonomous vehicle detects a stranded vehicle, the autonomous vehicle can communicate information about the stranded vehicle to emergency personnel. Additionally, in some examples, an autonomous vehicle can stop behind a stranded vehicle and display hazard lights or other lighting to warn others of the presence of the stranded vehicle. Similarly, in some examples, an autonomous vehicle fleet may utilize other services and/or external support to aid in recovery. For instance, if no autonomous vehicles are available to transport passengers in a stranded autonomous vehicle, a ridehail service may request another vehicle, such as a taxi, to pick up the passengers. In another example, a ridehail service may request support from a different service and/or a different autonomous vehicle fleet. In some examples, an autonomous vehicle fleet service may call an external towing company to recover a stranded vehicle.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the external support disclosed in Rusciano with the vehicle taught in the combination of TAIEB with a reasonable expectation of success because it would have recovered a stranded vehicle as taught by Rusciano.
Claims 35 and 36 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220076036 A1 to TAIEB; Yoav et al. (hereinafter Taieb cited in the 01/13/2025 IDS) in view of US 20200238996 A1 to Pendleton; Scott D. et al. (hereinafter Pendleton) and in further view of US 20190332875 A1 to Vallespi-Gonzalez; Carlos et al. (hereinafter Vallespi) as applied to the claims above in view of US 20210261152 A1 to MEIJBURG; Maria Antoinette et al. (hereinafter MEIJBURG) as applied to the claims above in view of US 20220009491 A1 to Taruoka; Yutaka et al. (hereinafter Taruoka).
Regarding claim 35 The combination of TAIEB does not appear to expressly disclose however in analogous art Taruoka teaches the method wherein the traffic light information comprises:
a current state of the traffic light at the intersection and a remaining time for the current state of the traffic light at the intersection (in for example, paras:
“[0029] Referring now to FIG. 2, a vehicle 100 in communication with a traffic light 20 is schematically illustrated. A traffic light typically has scheduled state information, which is the order in which the different lights are illuminated and an amount of time each light is illuminated. In the illustration of FIG. 2, the traffic light provides a time remaining Tr for each colored light. For example, the green light 21 may currently be illuminated and have a time remaining Tr of 15 seconds until the next state. The next state is the yellow state that has a time remaining Tr of 5 seconds. The third state in the sequence is the red state that has a time remaining Tr of 30 seconds.
[0045] A traffic light detection module 305 is configured to detect the presence of a traffic light as the vehicle approaches an intersection and receives traffic light state information. The traffic light detection module 305 may detect a traffic light by any means. In the example of FIG. 8, the traffic light detection module 305 receives as input traffic light state information 302 from V2I signals and image data from one or more camera sensors 303. When the vehicle receives traffic light state information, it is indicative that the vehicle is approaching a traffic light. Additionally, any known or yet-to-be-developed object detection algorithm may be performed on the image data to detect an upcoming traffic light. The output of the traffic light detection module 305 is all or some of the traffic light state information, which includes the remaining time for the states of the traffic light. Thus, the traffic light detection module 305 publishes not only the current signal state of the remaining time of the current state and, in some embodiments, the remaining time for the other states.
[0047] The example system 300 further includes a map module 307 that maps the traffic light state, the current traffic light state remaining time, the vehicle state (e.g., it receives the parameters of the vehicle, such as velocity, acceleration, and the like), and localized data on a map. The map module 307 may access one or more maps of the environment, such as a high definition map. The map module 307 outputs a position of the vehicle with respect to map data as well as the state of the vehicle.“) and figures
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It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings disclosed in Taruoka with the autonomous vehicle taught in the combination of TAIEB with a reasonable expectation of success because it would have improved the autonomous vehicle’s transitions through intersections as taught by Taruoka Para(s):
“[0006] Accordingly, alternative systems and methods for controlling vehicles through an intersection are desired.”.
Regarding claim 36 and the limitation the method of claim 35, wherein determining the traffic light information at the intersection comprises:
determining the remaining time for the current state of the traffic light at the intersection based on a time point of a state change of the traffic light immediately preceding the current state and a predetermined time duration for the current state of the traffic light (see the obviousness to combine and the rejection of corresponding parts of claim 35 above incorporated herein by reference and especially paras:
“[0036] Thus, embodiments of the present disclosure are configured to manipulate the velocity of the vehicle to modify a size of the zone of interest by determining a distance of the vehicle with respect to the intersection, determining a velocity of the vehicle, receiving traffic light state information, and calculating the zone of interest using the distance of the vehicle to the intersection, the velocity of the vehicle, and the traffic light state information.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings disclosed in Taruoka with the autonomous vehicle taught in the combination of TAIEB with a reasonable expectation of success because it would have improved the autonomous vehicle’s transitions through intersections as taught by Taruoka Para(s):
“[0006] Accordingly, alternative systems and methods for controlling vehicles through an intersection are desired.”.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 11, 12, 16 and 17 of U.S. Patent No. 12211288 B2.
Although the claims at issue are not identical, they are not patentably distinct from each other as shown by the side by side comparison below. Those claims not listed below are rejected for at least depending from a rejected base claim.
CLAIMS OF THE INSTANT APPLICATION
CLAIMS OF U.S. PATENT US 12211288 B2
1. a method, comprising:
deriving, with at least one processor, a first state of a traffic light at an intersection a vehicle is approaching, according to first detection data acquired by a first traffic light detection (TLD) system;
deriving, with the at least one processor, a second state of the traffic light at the intersection, according to second detection data acquired by a second TLD system that is independent from the first TLD system;
determining, with the at least one processor, traffic light information at the intersection based on at least one of
(i) the first state or
(ii) a result of checking whether the first state is same as the second state; and
causing, with the at least one processor, the vehicle to operate in accordance with the determined traffic light information at the intersection,
wherein deriving the second state of the traffic light at the intersection according to the second detection data comprises:
inferring cross traffic information according to the second detection data, and deriving the second state of the traffic light based on the inferred cross traffic information,
and wherein the cross traffic information comprises at least one of:
whether a field of view of a cross traffic is occluded,
the cross traffic comprising one or more other vehicles at a cross road segment different from a road segment toward which the vehicle is approaching,
whether the cross traffic is stopped,
whether a distance between the cross traffic and the intersection is greater than a predetermined limit,
or whether a speed of the cross traffic is decreasing.
1. A method, comprising:
deriving, with at least one processor, a first state of a traffic light at an intersection a vehicle is approaching, according to first detection data acquired by a first traffic light detection (TLD) system;
deriving, with the at least one processor, a second state of the traffic light at the intersection, according to second detection data acquired by a second TLD system that is independent from the first TLD system; determining, with the at least one processor, traffic light information at the intersection based on at least one of
(i) the first state or (ii) a result of checking whether the first state is same as the second state, wherein determining the traffic light information at the intersection comprises:
determining whether the first state is a specified state, when the first state is different from the specified state,
determining that the traffic light information at the intersection based on the first state, and when the first state is the specified state, checking whether the first state is same as the second state and determining the traffic light information at the intersection based on the result of the checking;
and causing, with the at least one processor, the vehicle to operate in accordance with the determined traffic light information at the intersection.
11. The method of claim 9, wherein deriving the second state of the traffic light at the intersection according to the second detection data comprises:
inferring cross traffic information according to the second detection data, and deriving the second state of the traffic light based on the inferred cross traffic information.
12. The method of claim 11, wherein the cross traffic information comprises at least one of: whether a field of view of a cross traffic is occluded,
the cross traffic comprising one or more other vehicles at a cross road segment different from a road segment toward which the vehicle is approaching,
whether the cross traffic is stopped,
whether a distance between the cross traffic and the intersection is greater than a predetermined limit,
or whether a speed of the cross traffic is decreasing.
38. A system, comprising: at least one processor, and at least one non-transitory storage media storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising: deriving a first state of a traffic light at an intersection a vehicle is approaching, according to first detection data acquired by a first traffic light detection (TLD) system; deriving a second state of the traffic light at the intersection, according to second detection data acquired by a second TLD system that is independent from the first TLD system; determining traffic light information at the intersection based on at least one of (i) the first state or (ii) a result of checking whether the first state is same as the second state; and causing the vehicle to operate in accordance with the determined traffic light information at the intersection, wherein deriving the second state of the traffic light at the intersection according to the second detection data comprises: inferring cross traffic information according to the second detection data, and deriving the second state of the traffic light based on the inferred cross traffic information, and wherein the cross traffic information comprises at least one of: whether a field of view of a cross traffic is occluded, the cross traffic comprising one or more other vehicles at a cross road segment different from a road segment toward which the vehicle is approaching, whether the cross traffic is stopped, whether a distance between the cross traffic and the intersection is greater than a predetermined limit, or whether a speed of the cross traffic is decreasing.
16. A system, comprising: at least one processor, and at least one non-transitory storage media storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising: deriving a first state of a traffic light at an intersection a vehicle is approaching, according to first detection data acquired by a first traffic light detection (TLD) system; deriving a second state of the traffic light at the intersection, according to second detection data acquired by a second TLD system that is independent from the first TLD system; determining traffic light information at the intersection based on at least one of (i) the first state or (ii) a result of checking whether the first state is same as the second state, wherein determining the traffic light information at the intersection comprises: determining whether the first state is a specified state, when the first state is different from the specified state, determining that the traffic light information at the intersection based on the first state, and when the first state is the specified state, checking whether the first state is same as the second state and determining the traffic light information at the intersection based on the result of the checking; and causing the vehicle to operate in accordance with the determined traffic light information at the intersection.
40. At least one non-transitory storage media storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising: deriving a first state of a traffic light at an intersection a vehicle is approaching, according to first detection data acquired by a first traffic light detection (TLD) system; deriving a second state of the traffic light at the intersection, according to second detection data acquired by a second TLD system that is independent from the first TLD system; determining traffic light information at the intersection based on at least one of (i) the first state or (ii) a result of checking whether the first state is same as the second state; and causing the vehicle to operate in accordance with the determined traffic light information at the intersection, wherein deriving the second state of the traffic light at the intersection according to the second detection data comprises: inferring cross traffic information according to the second detection data, and deriving the second state of the traffic light based on the inferred cross traffic information, and wherein the cross traffic information comprises at least one of: whether a field of view of a cross traffic is occluded, the cross traffic comprising one or more other vehicles at a cross road segment different from a road segment toward which the vehicle is approaching, whether the cross traffic is stopped, whether a distance between the cross traffic and the intersection is greater than a predetermined limit, or whether a speed of the cross traffic is decreasing.
17. At least one non-transitory storage media storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising: deriving a first state of a traffic light at an intersection a vehicle is approaching, according to first detection data acquired by a first traffic light detection (TLD) system; deriving a second state of the traffic light at the intersection, according to second detection data acquired by a second TLD system that is independent from the first TLD system; determining traffic light information at the intersection based on at least one of (i) the first state or (ii) a result of checking whether the first state is same as the second state, wherein determining the traffic light information at the intersection comprises: determining whether the first state is a specified state, when the first state is different from the specified state, determining that the traffic light information at the intersection based on the first state, and when the first state is the specified state, checking whether the first state is same as the second state and determining the traffic light information at the intersection based on the result of the checking; and causing the vehicle to operate in accordance with the determined traffic light information at the intersection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL LAWSON GREENE JR whose telephone number is (571)272-6876. The examiner can normally be reached on MON-THUR 7-5:30PM (EST).
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/DANIEL L GREENE/Primary Examiner, Art Unit 3665 20260529
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