70 citations · 95 across the 7 of their papers we have counts for
7 papers
Reachable Set-based Path Planning for Automated Vertical Parking System
In Hyuk Oh, Ju Won Seo, Jin Sung Kim +1
This paper proposes a local path planning method with a reachable set for Automated vertical Parking Systems (APS). First, given a parking lot layout with a goal position, we defin…
Classification Method of Road Surface Condition and Type with LiDAR Using Spatiotemporal Information
Ju Won Seo, Jin Sung Kim, Chung Choo Chung
This paper proposes a spatiotemporal architecture with a deep neural network (DNN) for road surface conditions and types classification using LiDAR. It is known that LiDAR provides…
A Nonlinear Proportional Integral Disturbance Observer and Motion Control Technique for Permanent Magnet Synchronous Motors
Yong Woo Jeong, Chung Choo Chung
In this paper, we present a Nonlinear-Proportional Integrator (N-PI) disturbance observer (DOB) to enhance the motion tracking of the performance of a surface-mounted Permanent Mag…
Robust Control for Lane Keeping System Using Linear Parameter Varying Approach with Scheduling Variables Reduction
Ying Shuai Quan, Jin Sung Kim, Chung Choo Chung
This paper presents a robust controller using a Linear Parameter Varying (LPV) model of the lane-keeping system with parameter reduction. Both varying vehicle speed and roll motion…
Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture
Seong Hyeon Park, ByeongDo Kim, Chang Mook Kang +2
In this paper, we propose a deep learning based vehicle trajectory prediction technique which can generate the future trajectory sequence of surrounding vehicles in real time. We e…
Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Network
ByeoungDo Kim, Chang Mook Kang, Seung Hi Lee +4
In this paper, we propose an efficient vehicle trajectory prediction framework based on recurrent neural network. Basically, the characteristic of the vehicle's trajectory is diffe…
Autonomous Braking System via Deep Reinforcement Learning
Hyunmin Chae, Chang Mook Kang, ByeoungDo Kim +3
In this paper, we propose a new autonomous braking system based on deep reinforcement learning. The proposed autonomous braking system automatically decides whether to apply the br…