4 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…