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公开(公告)号：US11902611B2
公开(公告)日：2024-02-13
申请号：US17683798
申请日：2022-03-01
发明人： Siqi Huang , Haoxin Wang , Akila C. Ganlath , Prashant Tiwari
IPC分类号： H04N21/414 , H04N21/24 , H04N21/466
CPC分类号： H04N21/41422 , H04N21/2407 , H04N21/4666
摘要： A method is provided including receiving a planned route of a vehicle and a request to download content from a cloud server, the planned route traveling through an area covered by a plurality of edge servers, determining a state comprising possible connections between the vehicle and each of the plurality of edge servers at a plurality of time steps during the planned route, inputting the state to a trained model, the model being trained to output an action comprising a partition of the content across the plurality of edge servers that minimizes latency of transmission of the content from the cloud server to the vehicle via the plurality of edge servers, based on the state, and partitioning the content across the plurality of edge servers based on the action out by the trained mode.
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12.发明授权公开(公告)号：US11688208B2
公开(公告)日：2023-06-27
申请号：US16939409
申请日：2020-07-27
发明人： Haoxin Wang , BaekGyu Kim
CPC分类号： G07C5/008 , B60W40/09 , G06F9/4881 , G07C5/0816
摘要： System, methods, and other embodiments described herein relate to improving scheduling tasks within an edge-computing environment. In one embodiment, a method includes, upon establishing a communication connection with a vehicle by an edge device of the edge-computing environment, collecting offloading information about the vehicle including task information describing at least a vehicle task that is to be offloaded to the edge device and context information about aspects relating to operation of the vehicle. The method includes triggering offloading of the vehicle task to the edge device in response to determining that at least the context information satisfies a scheduling threshold. The method includes providing, by the edge device, a result of executing the vehicle task to the vehicle.
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公开(公告)号：US20220391634A1
公开(公告)日：2022-12-08
申请号：US17338884
申请日：2021-06-04
发明人： Haoxin Wang , Akila C. Ganlath , Nejib Ammar , Onur Altintas , Prashant Tiwari , Takayuki Shimizu , BaekGyu Kim
摘要： Systems and methods for scheduling environment perception-based data offloading for numerous connected vehicles are disclosed. In one embodiment, a method for offloading data includes capturing an image of a view of interest from a vehicle, segmenting the image into a plurality of blocks, and determining a scheduling priority for each of one or more blocks among the plurality of blocks based on block values, wherein the block values relate to one or more objects of interest contained in each of the one or more blocks. The method further includes offloading, from the vehicle to a server, one or more blocks based on the scheduling priority of the one or more blocks.
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14.发明公开公开(公告)号：US20240187555A1
公开(公告)日：2024-06-06
申请号：US18073839
申请日：2022-12-02
发明人： Yitao Chen , Dawei Chen , Haoxin Wang , Kyungtae Han
CPC分类号： H04N7/181 , G01C21/3691 , G01C21/3893 , G01C21/3896
摘要： A camera view share system is provided. The camera view share system includes a road-side unit. The road-side unit includes a controller configured to receive a request for a bird-eye-view map from an ego vehicle, the bird-eye-view map including a plurality of vehicles, transmit the bird-eye-view map to the ego vehicle, receive a selection of a target vehicle among the plurality of vehicles from the ego vehicle, and transmit a camera feed related to the target vehicle to the ego vehicle.
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公开(公告)号：US20240127105A1
公开(公告)日：2024-04-18
申请号：US17965138
申请日：2022-10-13
发明人： Yitao Chen , Haoxin Wang , Dawei Chen , Kyungtae Han
CPC分类号： G06N20/00 , B60W30/12 , G06K9/6256 , B60W2756/10
摘要： A system for contribution-aware federated learning is provided. The system includes a server and a plurality of vehicles. Each of the plurality of vehicles includes a controller programmed to: train a local machine learning model using first local data; obtain metadata for hardware elements of corresponding vehicle; transmit the trained local machine learning model and the metadata to a server; receive an aggregated machine learning model from the server; and train the aggregated machine learning model using second local data. The server generates the aggregated machine learning model based on the trained local machine learning models and the metadata received from the plurality of vehicles.
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公开(公告)号：US11880428B2
公开(公告)日：2024-01-23
申请号：US17525129
申请日：2021-11-12
发明人： Siqi Huang , Haoxin Wang , Akila C. Ganlath , Prashant Tiwari
摘要： A server includes a controller programmed to obtain information about a first perception model installed in a vehicle. The controller is further programmed to determine a value of updating the first perception model. The controller is further programmed to determine whether the first perception model needs to be updated to a second perception model based on the value of updating the first perception model. The controller is further programmed to transmit the second perception model to the vehicle in response to determining that the first perception model needs to be updated to the second perception model.
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17.发明申请公开(公告)号：US20230035297A1
公开(公告)日：2023-02-02
申请号：US17391588
申请日：2021-08-02
发明人： Yuhan Kang , Haoxin Wang , BaekGyu Kim
摘要： A system for coordinating data offloading includes a controller. The controller is configured to at least receive a set of data metrics corresponding to a set of vehicles, generate a set of vehicle clusters based on the set of data metrics, determine a thread distribution based on the set of vehicle clusters, and transmit a data offloading control signal to each of the set of vehicles according to the thread distribution.
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公开(公告)号：US20230032183A1
公开(公告)日：2023-02-02
申请号：US17388645
申请日：2021-07-29
发明人： Yuhan Kang , Haoxin Wang , BaekGyu Kim
摘要： System, methods, and other embodiments described herein relate to selecting servers and allocating resources concurrently for offloading computing tasks from vehicles. In one embodiment, a method includes acquiring characteristics of a vehicle and a server for an offloading request, wherein the offloading request is associated with a computing task of the vehicle. The method also includes, upon satisfying criteria for optimization associated with executing the computing task remotely, determining server selection and resource allocation by processing the characteristics using modeling. The method also includes communicating the server selection and the resource allocation to the vehicle.
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19.发明授权公开(公告)号：US11427215B2
公开(公告)日：2022-08-30
申请号：US16944522
申请日：2020-07-31
发明人： Haoxin Wang , BaekGyu Kim
IPC分类号： B60W50/00 , B60W50/06 , H04L67/1014 , H04W4/44 , G06F9/48 , G06N3/08 , G01C21/00 , B60W30/14
摘要： Systems and methods described herein relate to generating a task offloading strategy for a vehicular edge-computing environment. One embodiment simulates a vehicular edge-computing environment in which one or more vehicles perform computational tasks whose data is partitioned into segments and performs, for each of a plurality of segments, a Deep Reinforcement Learning (DRL) training procedure that includes receiving state-space information regarding the one or more vehicles and one or more intermediate network nodes; inputting the state-space information to a policy network; generating, from the policy network, an action concerning a current segment; and assigning a reward to the policy network for the action in accordance with a predetermined reward function. This embodiment produces, via the DRL training procedure, a trained policy network embodying an offloading strategy for segmentation offloading of computational tasks from vehicles to one or more of an edge server and a cloud server.
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公开(公告)号：US20220034670A1
公开(公告)日：2022-02-03
申请号：US16944645
申请日：2020-07-31
发明人： Haoxin Wang , BaekGyu Kim
摘要： Systems and methods described herein relate to simulating edge-computing deployment in diverse terrains. One embodiment receives a layer-selection input specifying which layers among a plurality of layers in a simulation model of an edge-computing deployment are to be included in a simulation experiment; receives a set of input parameters for each of the layers specified by the layer-selection input, the set of input parameters for one of the layers specified by the layer-selection input including selection of a vehicular application whose performance in the edge-computing deployment is to be evaluated via the simulation experiment; executes the simulation experiment in accordance with the layer-selection input and the set of input parameters for each of the layers specified by the layer-selection input; and outputs, from the simulation experiment, performance data for the selected vehicular application in the edge-computing deployment.
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