DETAILED ACTION
Notice of AIA Status
The present application is being examined under the AIA the first inventor to file provisions.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/18/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
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.
Claims 1, 5, and 9-10, are rejected under 35 U.S.C. 102(a) (1) as being anticipated by Chao et al. (US 20130101159 A1) hereafter referenced as Chao.
Regarding claim 1, Chao teaches an information providing system comprising (Fig. 7, Paragraph [0010]- Chao discloses an apparatus for determining pedestrian traffic is offered. The apparatus includes a memory and a processor(s) coupled to the memory.):
at least one memory configured to store instructions (Fig. 2B, Paragraph [0041]- Chao discloses the server 290 includes a memory 270 for storing instructions for pedestrian traffic determination and a processor 280 for executing those instructions.);
and at least one processor configured to execute the instructions to (Fig. 2B, Paragraph [0041]- Chao discloses the server 290 includes a memory 270 for storing instructions for pedestrian traffic determination and a processor 280 for executing those instructions.):
measure a retention amount of pedestrians around an intersection based on images from a camera installed around the intersection (Fig. 3, Paragraph [0055]- Chao discloses using person detection and person tracking techniques, the amount of pedestrian traffic at certain locations may be determined/estimated for a future time. A traffic map can then be derived based on the number of individuals at each location.);
create a first map in which the retention amount of pedestrians around the intersection is represented by a marker on a map of the intersection (Fig. 2B, Paragraph [0044]- Chao discloses the pedestrian traffic information 275 may be sent as a colored heat map of a venue or regions around the current position of the mobile device 108 or user. The pedestrian traffic information 275 may be sent as a colored routability map of a current venue with different colors indicating different traffic conditions. Further in Fig. 5, Paragraph [0072]- Chao discloses a person moving toward the intersection recorded by the camera 402 may be approaching from the right hand side, indicated by the solid line marked with a "1".);
and output the first map to a predetermined output destination (Fig. 2B, Paragraph [0042]- Chao discloses the pedestrian traffic service 260, through the annotation database 264 or otherwise, may communicate with a position computing service 268 which may provide location information to a mobile device 108.).
Regarding claim 5, Chao teaches the information providing system according to claim 1, Chao further teaches wherein the at least one processor is further configured to execute the instructions to: record a measurement result of the retention amount of pedestrians around the intersection as time-series data (Fig. 2B, Paragraph [0044]- Chao discloses the pedestrian traffic information 275 may include information regarding pedestrian traffic at a location (including amount of traffic, direction of traffic, flow of traffic, etc.), pedestrian traffic at a location over time, estimated pedestrian traffic at a location, and/or route guidance information (including an estimated delay along a route, a preferred time to travel along a route, alternate times to travel along a route, alternate route selection, etc.).);
and create a map in which a change in the retention amount of pedestrians around the intersection can be read using the time-series data as the first map (Fig. 4C, Paragraph [0069]- Chao discloses traffic objects on a map may be continuously updated, which would allow a better computation of routes using temporal information. Improved or optimal routes may be calculated based on existing traffic and/or expected traffic. For example, a shortcut through a cafeteria may be undesired during lunch hour, but preferred at 3 p.m.).
Regarding claim 9, Chao teaches an information providing method comprising (Fig. 6, Paragraph [0077]- Chao discloses as shown in FIG. 6, a device may perform a method for determining pedestrian traffic.):
measuring a retention amount of pedestrians around an intersection based on images from a camera installed around the intersection (Fig. 3, Paragraph [0055]- Chao discloses using person detection and person tracking techniques, the amount of pedestrian traffic at certain locations may be determined/estimated for a future time. A traffic map can then be derived based on the number of individuals at each location.);
creating a first map in which the retention amount of pedestrians around the intersection is represented by a marker on a map of the intersection (Fig. 2B, Paragraph [0044]- Chao discloses the pedestrian traffic information 275 may be sent as a colored heat map of a venue or regions around the current position of the mobile device 108 or user. The pedestrian traffic information 275 may be sent as a colored routability map of a current venue with different colors indicating different traffic conditions. Further in Fig. 5, Paragraph [0072]- Chao discloses a person moving toward the intersection recorded by the camera 402 may be approaching from the right hand side, indicated by the solid line marked with a "1".);
and outputting the first map to a predetermined output destination (Fig. 2B, Paragraph [0042]-Chao discloses the pedestrian traffic service 260, through the annotation database 264 or otherwise, may communicate with a position computing service 268 which may provide location information to a mobile device 108.).
Regarding claim 10, Chao teaches a non-transitory program recording medium storing a program for causing a computer capable of acquiring images from a camera installed around an intersection to execute (Fig. 1, Paragraph [0009]- Chao discloses the computer program product includes a non-transitory computer-readable medium having non-transitory program code recorded thereon.):
a process of measuring a retention amount of pedestrians around the intersection based on the images from the camera (Fig. 3, Paragraph [0055]- Chao discloses using person detection and person tracking techniques, the amount of pedestrian traffic at certain locations may be determined/estimated for a future time. A traffic map can then be derived based on the number of individuals at each location.);
a process of creating a first map in which the retention amount of pedestrians around the intersection is represented by a marker on a map of the intersection (Fig. 2B, Paragraph [0044]- Chao discloses the pedestrian traffic information 275 may be sent as a colored heat map of a venue or regions around the current position of the mobile device 108 or user. The pedestrian traffic information 275 may be sent as a colored routability map of a current venue with different colors indicating different traffic conditions. Further in Fig. 5, Paragraph [0072]- Chao discloses a person moving toward the intersection recorded by the camera 402 may be approaching from the right hand side, indicated by the solid line marked with a "1".);
and a process of outputting the first map to a predetermined output destination (Fig. 2B, Paragraph [0042]- Chao discloses the pedestrian traffic service 260, through the annotation database 264 or otherwise, may communicate with a position computing service 268 which may provide location information to a mobile device 108.).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2-3 are rejected under 35 U.S.C 103 as being unpatentable over Chao et al. (US 20130101159 A1) hereafter referenced as Chao in view of Gatter et al. (US 20170177192 A1) hereafter referenced as Gatter.
Regarding claim 2, Chao teaches the information providing system according to claim 1, Chao further teaches wherein the at least one processor is further configured to execute the instructions to:
measure the retention amount of pedestrians in each transverse direction (Fig. 4B, Paragraph [0061]- Chao discloses each of those cameras may be associated with the node of the intersection and with multiple route segments intersecting the node (though each individual camera may be associated with different route segments depending on the direction the camera is facing). A route segment may also have one or more camera objects in its annotation layer. Further in Fig. 4C Paragraph [0063]- Chao discloses Each camera object may be associated with a variety of metadata including location coordinates (e.g., x, y, and/or z position), route segment(s), location type (e.g., intersection, stairs, etc.), traffic object, etc. Types of traffic objects include time (start, end, duration), total number of faces detected, distribution of pedestrian traffic on each route, direction of traffic, or other information.),
Chao fails to explicitly teach create, as the first map, a map in which the retention amount of pedestrians in each transverse direction is represented by a size of the marker on the map of the intersection.
However, Gatter explicitly teaches create, as the first map, a map in which the retention amount of pedestrians in each transverse direction is represented by a size of the marker on the map of the intersection (Fig. 2, Paragraph [0017]- Gatter discloses the mapping application can cluster the locational information items into a few clusters, each cluster representing multiple locational information items.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Chao of an information providing system comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: measure a retention amount of pedestrians around an intersection based on images from a camera installed around the intersection with the teachings of Gatter create, as the first map, a map in which the retention amount of pedestrians in each transverse direction is represented by a size of the marker on the map of the intersection.
Wherein having Chao’s system of pedestrian traffic mapping wherein create, as the first map, a map in which the retention amount of pedestrians in each transverse direction is represented by a size of the marker on the map of the intersection.
The motivation behind the modification would have been to have an easier to understand map, since both Chao and Gatter are both systems for that use mapping of multiple data points. Wherein Chao’s system provides a way to improve route planning, while Whiting’s system provides a way to improve user understanding of map by solving overcrowding. Please see Chao et al. (US 20130101159 A1) Paragraph [0069] and Gatter et al. (US 20170177192 A1) Paragraphs [0017-18].
Regarding claim 3, Chao in view of Gatter teaches the information providing system according to claim 2, Chao further teaches wherein the at least one processor is further configured to execute the instructions to: estimate a direction of the pedestrian based on the images from the camera (Fig. 2B, Paragraph [0044]- Chao discloses the pedestrian traffic information 275 may include information regarding pedestrian traffic at a location (including amount of traffic, direction of traffic, flow of traffic, etc.), pedestrian traffic at a location over time, estimated pedestrian traffic at a location, and/or route guidance information (including an estimated delay along a route, a preferred time to travel along a route, alternate times to travel along a route, alternate route selection, etc.).);
and measure the retention amount of pedestrians in each transverse direction based on the estimated direction of the pedestrian (Fig. 4C, Paragraph [0070]- Chao discloses for a camera on an intersection, the route a person takes may be estimated based on the trajectory of the person's motion on the video. In this scenario, trajectory means the person's likely direction from the specific intersection (i.e. left, right, straight, etc.) Over time, a statistical model of the traffic distribution at that intersection may be built. For example, for a particular intersection at a particular time, 20% of people go left, 10% go right, and 70% go straight. In another aspect, the speed/flow of the traffic may be observed.).
Claims 4 are rejected under 35 U.S.C 103 as being unpatentable over Chao et al. (US 20130101159 A1) hereafter referenced as Chao in view of Gatter et al. (US 20170177192 A1) hereafter referenced as Gatter and Whiting et al. (US 9460613 B1) hereafter referenced as Whiting.
Regarding claim 4, Chao in view of Gatter teaches the information providing system according to claim 2, Chao in view of Gatter fails to explicitly teach wherein the at least one processor is further configured to execute the instructions to: estimate a position of the pedestrian in a region of a sidewalk at the intersection based on the images from the camera; and measure the retention amount of pedestrians in each transverse direction based on the estimated position of the pedestrian in the region of the sidewalk.
However, Whiting explicitly teaches wherein the at least one processor is further configured to execute the instructions to: estimate a position of the pedestrian in a region of a sidewalk at the intersection based on the images from the camera (Fig. 5, Column 6 Lines [0005-9]- Whiting discloses the object movement analysis 162 determines the area of the field of view 112 where pedestrians 102 typically enter the roadway, by identifying and differentiating pedestrians 102 from other roadway users and tracking their position as the move through the field of view 112.);
and measure the retention amount of pedestrians in each transverse direction based on the estimated position of the pedestrian in the region of the sidewalk (Fig. 5, Column 6 Lines [0005-9]- Whiting discloses the object movement analysis 162 determines the area of the field of view 112 where pedestrians 102 typically enter the roadway, by identifying and differentiating pedestrians 102 from other roadway users and tracking their position as the move through the field of view 112. Further in Fig. 5, Column 14 Lines [0032-37]- Whiting discloses in the modeling approach described above, every zone requires the computation of a left and a right border region of interest 103. If two zones are considered horizontal neighbors, then they will share a border region of interest 103, and the area between the zones is established as the border region of interest 103.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Chao in view of Gatter of an information providing system comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: measure a retention amount of pedestrians around an intersection based on images from a camera installed around the intersection with the teachings of Whiting wherein the at least one processor is further configured to execute the instructions to: estimate a position of the pedestrian in a region of a sidewalk at the intersection based on the images from the camera; and measure the retention amount of pedestrians in each transverse direction based on the estimated position of the pedestrian in the region of the sidewalk.
Wherein having Chao’s system of pedestrian traffic mapping wherein the at least one processor is further configured to execute the instructions to: estimate a position of the pedestrian in a region of a sidewalk at the intersection based on the images from the camera; and measure the retention amount of pedestrians in each transverse direction based on the estimated position of the pedestrian in the region of the sidewalk.
The motivation behind the modification would have been to have a more accurate traffic management system, since both Chao and Whiting are both systems for traffic management. Wherein Chao’s system provides a way to improve route planning, while Whiting’s system provides a way to improve accuracy of traffic management. Please see Chao et al. (US 20130101159 A1) Paragraph [0069] and Whiting et al. (US 9460613 B1) Column 1 lines [0062-67] and Column 2 lines [0001-6].
Claims 6 are rejected under 35 U.S.C 103 as being unpatentable over Chao et al. (US 20130101159 A1) hereafter referenced as Chao in view of Whiting et al. (US 9460613 B1) hereafter referenced as Whiting.
Regarding claim 6, Chao teaches the information providing system according to claim 1, Chao further teaches wherein the at least one processor is further configured to execute the instructions to: create, for each of the detected pedestrians, a second map in which an arrow or a line segment indicating the position and the movement of the pedestrian is represented on the map of the intersection (Further in Fig. 5, Paragraph [0072]- Chao discloses as illustrated in example 1, a person moving toward the intersection recorded by the camera 402 may be approaching from the right hand side, indicated by the solid line marked with a "1". That person may either turn right, and head up in the illustrated indoor map, or the person may turn left, and head down in the illustrated indoor map. As illustrated in example 2, a person moving toward the intersection recorded by the camera 402 may be approaching from the left hand side, indicated by the solid line marked with a "2".).
Chao fails to explicitly teach detect a position and a movement of the pedestrian passing through the intersection based on the images from the camera installed around the intersection.
However, Whiting explicitly teaches detect a position and a movement of the pedestrian passing through the intersection based on the images from the camera installed around the intersection (Fig. 5, Column 6 Lines [0005-9]- Whiting discloses the object movement analysis 162 determines the area of the field of view 112 where pedestrians 102 typically enter the roadway, by identifying and differentiating pedestrians 102 from other roadway users and tracking their position as the move through the field of view 112. Further in Fig. 5, Column 6 Lines [0016-0020]- Whiting discloses once the pedestrian zone identification component 143 identifies normal pedestrian tracks 108 in the field of view 112, a boundary box is created and the area can then be used to collect additional data from various analytics, such as determining count, speed, trajectory, and grouping of pedestrians 102. (where in trajectory is a form of movement));
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Chao of an information providing system comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: measure a retention amount of pedestrians around an intersection based on images from a camera installed around the intersection with the teachings of Whiting detect a position and a movement of the pedestrian passing through the intersection based on the images from the camera installed around the intersection
Wherein having Chao’s system of pedestrian traffic mapping wherein detect a position and a movement of the pedestrian passing through the intersection based on the images from the camera installed around the intersection
The motivation behind the modification would have been to have a more accurate traffic management system, since both Chao and Whiting are both systems for traffic management. Wherein Chao’s system provides a way to improve route planning, while Whiting’s system provides a way to improve accuracy of traffic management. Please see Chao et al. (US 20130101159 A1) Paragraph [0069] and Whiting et al. (US 9460613 B1) Column 1 lines [0062-67] and Column 2 lines [0001-6].
Claims 7-8 are rejected under 35 U.S.C 103 as being unpatentable over Chao et al. (US 20130101159 A1) hereafter referenced as Chao in view of Whiting et al. (US 9460613 B1) hereafter referenced as Whiting and Sharifi et al. (Analysis and Modeling of Pedestrian Walking Behaviors Involving Individuals with Disabilities) hereafter referenced as Sharifi.
Regarding claim 7, Chao teaches the information providing system according to claim 1, Chao fails to explicitly teach wherein the at least one processor is further configured to execute the instructions to: detect a position and a movement of the pedestrian passing through the intersection based on the images from the camera installed around the intersection.
However, Whiting explicitly teaches wherein the at least one processor is further configured to execute the instructions to: detect a position and a movement of the pedestrian passing through the intersection based on the images from the camera installed around the intersection (Fig. 5, Column 6 Lines [0005-9]- Whiting discloses the object movement analysis 162 determines the area of the field of view 112 where pedestrians 102 typically enter the roadway, by identifying and differentiating pedestrians 102 from other roadway users and tracking their position as the move through the field of view 112. Further in Fig. 5, Column 6 Lines [0016-0020]- Whiting discloses once the pedestrian zone identification component 143 identifies normal pedestrian tracks 108 in the field of view 112, a boundary box is created and the area can then be used to collect additional data from various analytics, such as determining count, speed, trajectory, and grouping of pedestrians 102. (where in trajectory is a form of movement));
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Chao of an information providing system comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: measure a retention amount of pedestrians around an intersection based on images from a camera installed around the intersection with the teachings of Whiting wherein the at least one processor is further configured to execute the instructions to: detect a position and a movement of the pedestrian passing through the intersection based on the images from the camera installed around the intersection.
Wherein having Chao’s system of pedestrian traffic mapping wherein the at least one processor is further configured to execute the instructions to: detect a position and a movement of the pedestrian passing through the intersection based on the images from the camera installed around the intersection.
The motivation behind the modification would have been to have a more accurate traffic management system, since both Chao and Whiting are both systems for traffic management. Wherein Chao’s system provides a way to improve route planning, while Whiting’s system provides a way to improve accuracy of traffic management. Please see Chao et al. (US 20130101159 A1) Paragraph [0069] and Whiting et al. (US 9460613 B1) Column 1 lines [0062-67] and Column 2 lines [0001-6].
Chao in view of Whiting fails to explicitly teach create a time distance diagram representing a movement situation of the pedestrian in a past predetermined period of a crosswalk at the intersection in time series; and when the marker on the first map is selected and a predetermined operation is performed, output a time distance diagram of a crosswalk at a location indicated by the marker to the predetermined output destination.
However, Sharifi explicitly teaches create a time distance diagram representing a movement situation of the pedestrian in a past predetermined period of a crosswalk at the intersection in time series (Section 4.4 paragraph [0001]- Sharifi discloses time-space trajectories of pedestrian crowd dynamics are depicted in Fig. 4.2. These time-space diagrams were created by plotting the position of each participant, given at a distance from a reference point (e.g., entrance of the circuit) against time.);
and when the marker on the first map is selected and a predetermined operation is performed, output a time distance diagram of a crosswalk at a location indicated by the marker to the predetermined output destination (Section 5.4 Paragraph [0001]- Sharifi discloses the processing component makes it possible to extract the raw trajectory data for a selective area or selected time duration for all pedestrians or for a selective group of pedestrians (e.g. pedestrians with disabilities).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Chao in view of Whiting of an information providing system comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: measure a retention amount of pedestrians around an intersection based on images from a camera installed around the intersection with the teachings of Sharifi create a time distance diagram representing a movement situation of the pedestrian in a past predetermined period of a crosswalk at the intersection in time series; and when the marker on the first map is selected and a predetermined operation is performed, output a time distance diagram of a crosswalk at a location indicated by the marker to the predetermined output destination.
Wherein having Chao’s system of pedestrian traffic mapping wherein create a time distance diagram representing a movement situation of the pedestrian in a past predetermined period of a crosswalk at the intersection in time series; and when the marker on the first map is selected and a predetermined operation is performed, output a time distance diagram of a crosswalk at a location indicated by the marker to the predetermined output destination.
The motivation behind the modification would have been to have a more accurate model of pedestrian behaviors, since both Chao and Sharifi are both systems for tracking and analyzing pedestrian movement. Wherein Chao’s system provides a way to improve route planning, while Sharifi’s system provides a way to improve accuracy of the model of pedestrian behavior. Please see Chao et al. (US 20130101159 A1) Paragraph [0069] and Sharifi et al. (Analysis and Modeling of Pedestrian Walking Behaviors Involving Individuals with Disabilities) Section 7.3.2 Paragraph [0001].
Regarding claim 8, Chao in view of Whitin and Sharifi explicitly teaches the information providing system according to claim 7, Chao further teaches wherein the at least one processor is further configured to execute the instructions to: determine an attribute of a pedestrian passing through the intersection based on images from a camera installed around the intersection (Fig. 3, Paragraph [0056]- Chao discloses person detection may be performed through face detection or through any other suitable technique. Such detection techniques may include determining whether a video image includes features associated with an individual's face. Background portions of an image may be removed to isolate foreground portions which are processed to determine if facial features are detected. Skin color detection techniques may be used.);
Chao in view of Whiting fails to explicitly teach create a time distance diagram in which the attribute of the pedestrian can be identified.
However, Sharifi explicitly teaches create a time distance diagram in which the attribute of the pedestrian can be identified (Section 4.4 paragraph [0001]- Sharifi discloses it can be observed that 63 individuals without disabilities maintain a more conservative spacing from individuals with disabilities, and the time headway between individuals without disabilities is lower compared to the time headway between individuals without and with disabilities. In addition, the slope of the trajectories represents the speed of participants with the curved portions indicating speed changes.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Chao in view of Whiting of an information providing system comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: measure a retention amount of pedestrians around an intersection based on images from a camera installed around the intersection with the teachings of Sharifi create a time distance diagram in which the attribute of the pedestrian can be identified.
Wherein having Chao’s system of pedestrian traffic mapping wherein create a time distance diagram in which the attribute of the pedestrian can be identified.
The motivation behind the modification would have been to have a more accurate model of pedestrian behaviors, since both Chao and Sharifi are both systems for tracking and analyzing pedestrian movement. Wherein Chao’s system provides a way to improve route planning, while Sharifi’s system provides a way to improve accuracy of the model of pedestrian behavior. Please see Chao et al. (US 20130101159 A1) Paragraph [0069] and Sharifi et al. (Analysis and Modeling of Pedestrian Walking Behaviors Involving Individuals with Disabilities) Section 7.3.2 Paragraph [0001].
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered
pertinent to applicant`s disclosure.
Malkes et al. (US 20190333370 A1)- The present disclosure is directed to methods and apparatus that monitor pedestrian traffic and that adjust the behavior of traffic signals at intersections and “walk”-“do not walk” indicators associated with particular crosswalks. Methods and apparatus consistent with the present disclosure may receive image or sensor data, may monitor the status of different traffic flow, and may adjust the timing of signal lights or walking indications as conditions change at an intersection. In certain instances, a traffic controller at one intersection may receive information collected by other traffic controllers along a set of streets that lead to a particular intersection. Traffic controllers that receive images of an intersection may identify partition the intersection into a set of safe and unsafe zones as those traffic controllers identify when pedestrians can safely cross an intersection....................Please see Fig. 1. Abstract.
Caminiti et al. (US 20100100324 A1)- In various examples, a deep neural network (DNN) is trained—using image data alone—to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an ego-vehicle and/or sensor data from any number of depth predicting sensors—such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. The DNN may be trained using two or more loss functions each corresponding to a particular portion of the environment that depth is predicted for, such that—in deployment—more accurate depth estimates for objects, obstacles, and/or the detected free-space boundary are computed by the DNN. In some embodiments, a sampling algorithm may be used to sample depth values corresponding to an input resolution of the DNN from a predicted depth map of the DNN at an output resolution of the DNN....................Please see Fig. 1. Abstract.
Packer et al. (US 11577722 B1)- A vehicle computing system may implement techniques to predict behavior of objects detected by a vehicle operating in the environment. The techniques may include determining a feature with respect to a detected objects (e.g., likelihood that the detected object will impact operation of the vehicle) and/or a location of the vehicle and determining based on the feature a model to use to predict behavior (e.g., estimated states) of proximate objects (e.g., the detected object). The model may be configured to use one or more algorithms, classifiers, and/or computational resources to predict the behavior. Different models may be used to predict behavior of different objects and/or regions in the environment. Each model may receive sensor data as an input, and output predicted behavior for the detected object. Based on the predicted behavior of the object, a vehicle computing system may control operation of the vehicle......................Please see Fig. 1. Abstract.
Davies et al. (US 20210326699 A1)- One or more techniques and/or systems are provided for travel speed prediction. A spatial context of a prediction segment of a travel network for which a speed prediction is to be made is identified. The spatial context comprises one or more segments of the travel network that are part of trajectories of objects passing through the predication segment and that have predicted likelihoods of influencing travel speed along the prediction segment above a threshold. Features of the spatial context are formatted into a format compatible for input into the model based upon a structure of the model. The features are input into the model for processing using machine learning functionality to output the speed prediction for the prediction segment........................Please see Fig. 1. Abstract.
Zhang et al. (US 20190278276 A1)- According to some embodiments, a system performs an emergency stop when a speed planning optimization fails to generate a speed curve. In one embodiment, in response to an emergency stop request, the system generates one or more path-time analytical curves, where each of the one or more path-time curves is represented by a polynomial function. The system selects one of the path-time analytical curves to determine whether the selected path-time analytical curve satisfies a set of evaluation criteria. If the selected path-time analytical curve does not satisfy the set of evaluation criteria, the system selects a next one of the path-time analytical curves for evaluation. If the selected path-time analytical curve satisfies the set of evaluation criteria, the system generates a trajectory based on the selected path-time analytical curve to control the ADV during an emergency stop.........................Please see Fig. 1. Abstract.
Lin et al. (US 20210325207 A1)- The present disclosure provides a map updating method, system and a readable storage medium for autonomous driving. The method may include: a vehicle sends a local map request to a server, the local map request includes current location data of the vehicle, the vehicle includes an autonomous driving vehicle; the vehicle receives a first local map of the current location from the server, the first local map covers a first distance on the route of the vehicle; a sensor mounted on the vehicle collects first surrounding environment data of the vehicle during travelling along the first distance; and based on the first local map and the first surrounding environment data, the vehicle generates map update data and sends the data to the server.........................Please see Fig. 1. Abstract.
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/LUCIUS CAMERON GREEN ALLEN/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673