公开(公告)号：US20250095487A1

公开(公告)日：2025-03-20

申请号：US18404077

申请日：2024-01-04

Applicant: FOUNDATION OF SOONGSIL UNIVERSITY-INDUSTRY COOPERATION

Inventor： Min Hae KWON ,  Chan In EOM ,  Dong Su LEE

IPC: G08G1/0967 ,  B60W30/14 ,  B60W60/00 ,  G08G1/01

Abstract: A traffic control method and apparatus with an autonomous vehicle based on adaptive reward. A traffic control apparatus for an autonomous vehicle based on adaptive reward comprises an information observation unit that collects observation information from a sensing module of an autonomous vehicle or a roadside unit (RSU); a policy execution unit that decides on an action including adjusting acceleration and changing lanes of the autonomous vehicle based on the observation information and policy; and a reward determination unit that determines reward according to observation information at a next timestep according to the decision made, wherein reward in the reward determination unit includes penalty in an event of an accident and reward when driving, wherein the reward when driving includes an adaptive target speed reward term, a successful lane change reward term, and a safety distance compliance reward term that are adaptively determined according to road traffic.

公开(公告)号：US20240242088A1

公开(公告)日：2024-07-18

申请号：US18219691

申请日：2023-07-09

Applicant: FOUNDATION OF SOONGSIL UNIVERSITY-INDUSTRY COOPERATION

Inventor： Min Hae KWON ,  Mi Ru KIM ,  Hyo Seon KYE

IPC: G06N3/098

CPC classification number: G06N3/098

Abstract: Provided is a method of personalized federated learning performed by an electronic device. The method is performed by an electronic device including one or more processors, a communication circuit which communicates with an external device, and one or more memories storing at least one instruction executed by the one or more processors. The method may include, by the one or more processors, training a local model using local data, in which the local model as an artificial neural network model includes a first parameter set corresponding to a global parameter set and a second parameter set corresponding to a local parameter set, transmitting the first parameter set to the external device, receiving a 1-1st parameter set for renewing the first parameter set from the external device, changing the first parameter set included in the local model to the 1-1st parameter set, and training the local model including the 1-1st parameter set.

公开(公告)号：US20240241800A1

公开(公告)日：2024-07-18

申请号：US18219642

申请日：2023-07-07

Applicant: FOUNDATION OF SOONGSIL UNIVERSITY-INDUSTRY COOPERATION

Inventor： Min Hae KWON ,  Mi Ru KIM

IPC: G06F11/14

CPC classification number: G06F11/1471

Abstract: Provided is an anomaly detection method performed by an electronic device. The method performed by an electronic device including one or more processors, a communication circuit which communicates with an external device, and one or more memories storing at least one instruction executed by the one or more processors may include: by the one or more processors, receiving target data for discriminating whether an anomaly occurs, in which the target data includes a value for each of a plurality of features; inputting a value for at least one important feature among the plurality of features into an anomaly detection model, in which the at least one important feature is determined by important feature information received from the external device; and determining whether the target data is abnormal based on an output of the anomaly detection model.

公开(公告)号：US20250086496A1

公开(公告)日：2025-03-13

申请号：US18404143

申请日：2024-01-04

Applicant: FOUNDATION OF SOONGSIL UNIVERSITY-INDUSTRY COOPERATION

Inventor： Min Hae KWON ,  Mi Ru KIM ,  Hee Won PARK

IPC: G06N20/00

Abstract: A method and apparatus for augmenting knowledge using federated learning information. An apparatus for augmenting knowledge using federated learning information comprises a transceiver unit that receives local information including a global parameter of a local model, a local latent vector, and a local loss value from each of a plurality of individual devices, a data storage unit that stores the local information, a federated learning execution unit that collects a global parameter of the local model and generates a federated global parameter for a global model, and a large model learning unit that generates a true label approximate value for learning a large model using the local information and the federated global parameter, and learns the large model using a prediction result obtained by inputting the local latent vector into the large model and the true label approximate value.

公开(公告)号：US20250077943A1

公开(公告)日：2025-03-06

申请号：US18414026

申请日：2024-01-16

Applicant: FOUNDATION OF SOONGSIL UNIVERSITY-INDUSTRY COOPERATION

Inventor： Min Hae KWON ,  Dong Su LEE

IPC: G06N20/00 ,  G07C5/08

Abstract: An offline reinforcement learning-based foresighted decision-making apparatus and method for interaction between multiple agents. The offline reinforcement learning-based foresighted decision-making apparatus comprises a processor; and a memory connected to the processor, wherein the memory comprises program instructions for performing steps comprising collecting a raw data set about environment surrounding the multiple agents, processing the raw data set into a first data set containing at least one of state information, observation information, and action information in reinforcement learning, generating an episodic future data set from the first data set based on some of the state information, observation information, and action information, generating an episodic future data prediction model using the episodic future data set, and learning an optimal policy for decision-making for each of the multiple agents through offline reinforcement learning using the generated episodic future data prediction model or the episodic future data set.

公开(公告)号：US20240242596A1

公开(公告)日：2024-07-18

申请号：US18219628

申请日：2023-07-07

Applicant: FOUNDATION OF SOONGSIL UNIVERSITY-INDUSTRY COOPERATION

Inventor： Min Hae KWON ,  Chan In EOM ,  Dong Su LEE

IPC: G08G1/01 ,  G08G1/0967

CPC classification number: G08G1/0116 ,  G08G1/096725

Abstract: Provided is a method and an apparatus for determining a vehicle behavior, and more specifically, to a method and an apparatus for determining a vehicle behavior for bottleneck congestion control in a bottleneck section. Tn apparatus for determining a vehicle behavior may include an information collection unit collecting surrounding information of a target driving vehicle from a road side unit (RSU), a vehicle observation unit obtaining observation information based on the target driving vehicle from a sensing module mounted on the target driving vehicle, a reward determination unit determining a reward for the target driving vehicle through a reward function which uses the surrounding information and the observation information, a model training unit updating and training a decision making model through the reward, and a behavior determination unit determining a behavior of the target driving vehicle by inputting the observation information into the decision making model.