Osong Medical Innovation Foundation, Korea University Research and Business Foundation

Ajou University Industry-academic Cooperation Foundation

Samsung Electronics Co., Ltd.

Won Joon Yun

Joongheon Kim

SooHyun Park

Soyi Jung

Jae-Hyun Kim

Youn Kyu Lee

A processor-implemented method with virtual object rendering includes: determining a plurality of predictive trajectories of a first object according to a Gaussian random path based on a high-level model that is trained by hierarchical reinforcement learning; determining direction information of a second object according to subgoals corresponding to the predictive trajectories based on a low-level model that is trained by hierarchical reinforcement learning; determining direction information of the second object according to a subgoal corresponding to one of the predictive trajectories based on an actual trajectory of the first object; and rendering the second object, which is a virtual object, based on the determined direction information.

Method and apparatus with virtual object rendering

Provided are a drone taxi system based on multi-agent reinforcement learning and a drone taxi operation method using the same. The drone taxi system includes a plurality of drone taxies configured to receive call information including departure point information and destination information from passenger terminals present within a certain range and a control server configured to receive call information of passengers from each drone taxi, select a candidate passenger depending on whether a passenger is present, generate travel route information of each drone taxi from drone state information of the plurality of drone taxies through multi-agent reinforcement learning, and transmit the travel route information to the drone taxi.

Drone taxi system based on multi-agent reinforcement learning and drone taxi operation using the same

An object recognition method using queue-based model selection and optical flow in an autonomous driving environment includes preprocessing data through a dense flow in a matrix form by calculating an optical flow of images captured consecutively in time by a sensor for an autonomous vehicle, generating a confidence mask by generating a vectorized confidence threshold representing a probability that there is a moving object for each cell of the preprocessed matrix, determining whether there is a moving object on the images by mapping the images captured consecutively in time to the confidence mask, and selecting an object recognition model using a tradeoff constant between object recognition accuracy and queue stability in each time unit. Accordingly, it is possible to improve the performance of object recognition in an autonomous driving environment by applying the optical flow to the confidence threshold of the object recognition system.

Method for object recognition using queue-based model selection and optical flow in autonomous driving environment, recording medium and device for performing the method

Provided is a surveillance system employing a plurality of unmanned aerial vehicles (UAVs), the surveillance system showing improved surveillance performance while optimizing common energy consumption for computing of all the UAVs and also providing a stable visual monitoring service using autonomous mobility of the plurality of UAVs regardless of movement of an object to be monitored and action uncertainty of an adjacent UAV.

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Seoul, South Korea

Won Joon Yun