16 citations · 52 across the 14 of their papers we have counts for
14 papers
Post-Training and Test-Time Scaling of Generative Agent Behavior Models for Interactive Autonomous Driving
Hyunki Seong, Jeong-Kyun Lee, Heesoo Myeong +5
Learning interactive motion behaviors among multiple agents is a core challenge in autonomous driving. While imitation learning models generate realistic trajectories, they often i…
VLA-R: Vision-Language Action Retrieval toward Open-World End-to-End Autonomous Driving
Hyunki Seong, Seongwoo Moon, Hojin Ahn +2
Exploring open-world situations in an end-to-end manner is a promising yet challenging task due to the need for strong generalization capabilities. In particular, end-to-end autono…
Learning from Demonstration with Hierarchical Policy Abstractions Toward High-Performance and Courteous Autonomous Racing
Chanyoung Chung, Hyunki Seong, David Hyunchul Shim
Fully autonomous racing demands not only high-speed driving but also fair and courteous maneuvers. In this paper, we propose an autonomous racing framework that learns complex raci…
Words to Wheels: Vision-Based Autonomous Driving Understanding Human Language Instructions Using Foundation Models
Chanhoe Ryu, Hyunki Seong, Daegyu Lee +3
This paper introduces an innovative application of foundation models, enabling Unmanned Ground Vehicles (UGVs) equipped with an RGB-D camera to navigate to designated destinations…
Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity
Hyunki Seong, David Hyunchul Shim
We introduce MoNet, a novel functionally modular network for self-supervised and interpretable end-to-end learning. By leveraging its functional modularity with a latent-guided con…
Skill Q-Network: Learning Adaptive Skill Ensemble for Mapless Navigation in Unknown Environments
Hyunki Seong, David Hyunchul Shim
This paper focuses on the acquisition of mapless navigation skills within unknown environments. We introduce the Skill Q-Network (SQN), a novel reinforcement learning method featur…
Topological Exploration using Segmented Map with Keyframe Contribution in Subterranean Environments
Boseong Kim, Hyunki Seong, D. Hyunchul Shim
Existing exploration algorithms mainly generate frontiers using random sampling or motion primitive methods within a specific sensor range or search space. However, frontiers gener…
TempFuser: Learning Agile, Tactical, and Acrobatic Flight Maneuvers Using a Long Short-Term Temporal Fusion Transformer
Hyunki Seong, David Hyunchul Shim
Dogfighting is a challenging scenario in aerial applications that requires a comprehensive understanding of both strategic maneuvers and the aerodynamics of agile aircraft. The aer…
A Versatile Door Opening System with Mobile Manipulator through Adaptive Position-Force Control and Reinforcement Learning
Gyuree Kang, Hyunki Seong, Daegyu Lee +1
The ability of robots to navigate through doors is crucial for their effective operation in indoor environments. Consequently, extensive research has been conducted to develop robo…
An Autonomous System for Head-to-Head Race: Design, Implementation and Analysis; Team KAIST at the Indy Autonomous Challenge
Chanyoung Jung, Andrea Finazzi, Hyunki Seong +6
While the majority of autonomous driving research has concentrated on everyday driving scenarios, further safety and performance improvements of autonomous vehicles require a focus…
Model Parameter Identification via a Hyperparameter Optimization Scheme for Autonomous Racing Systems
Hyunki Seong, Chanyoung Chung, David Hyunchul Shim
In this letter, we propose a model parameter identification method via a hyperparameter optimization scheme (MI-HPO). Our method adopts an efficient explore-exploit strategy to ide…
Design, Field Evaluation, and Traffic Analysis of a Competitive Autonomous Driving Model in a Congested Environment
Daegyu Lee, Hyunki Seong, Seungil Han +3
Recently, numerous studies have investigated cooperative traffic systems using the communication among vehicle-to-everything (V2X). Unfortunately, when multiple autonomous vehicles…
A Resilient Navigation and Path Planning System for High-speed Autonomous Race Car
Daegyu Lee, Chanyoung Jung, Andrea Finazzi +2
This paper describes a resilient navigation and planning system used in the Indy Autonomous Challenge (IAC) competition. The IAC is a competition where full-scale race cars run aut…
Game-Theoretic Model Predictive Control with Data-Driven Identification of Vehicle Model for Head-to-Head Autonomous Racing
Chanyoung Jung, Seungwook Lee, Hyunki Seong +2
Resolving edge-cases in autonomous driving, head-to-head autonomous racing is getting a lot of attention from the industry and academia. In this study, we propose a game-theoretic…