6 papers
Triggering Generalist Reasoning via Predictive Uncertainty for Dual-System VLA
Hyemin Yang, Wooseong Jeong, Giwon Lee +1
Dual-system Vision-Language-Action (VLA) models improve real-time robotic control by pairing a slow, reasoning-capable generalist with a fast specialist action expert. However, exi…
VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation
Hoonhee Cho, Jae-Young Kang, Giwon Lee +3
End-to-end autonomous driving in urban environments requires robust decision-making under partial observability and complex multi-agent interactions. Severe occlusions and dense tr…
Bootstrapping a 4D LiDAR Annotation Tool from Video Foundation Models
Jihun Kim, Hyun-Kurl Jang, Hyemin Yang +3
Progress in 4D LiDAR segmentation is bottlenecked by data. Assigning temporally consistent labels across sparse point cloud sequences is costly and hard to scale, and every new tas…
HSDF-Lane: Height-Aligned Signed Distance Field with Semantic Lane Prior for 3D Lane Detection
Jiyong Boo, Byeongin Joung, Hyemin Yang +1
Monocular 3D lane detection plays a critical role in autonomous driving, yet recovering reliable 3D geometry from a single image remains challenging due to inherent depth ambiguity…
HEAT: Heterogeneous End-to-End Autonomous Driving via Trajectory-Guided World Models
Hoonhee Cho, Giwon Lee, Jae-Young Kang +3
End-to-end autonomous driving has emerged as a compelling alternative to traditional modular pipelines by directly mapping raw sensor data to driving actions. While recent approach…
VR-Drive: Viewpoint-Robust End-to-End Driving with Feed-Forward 3D Gaussian Splatting
Hoonhee Cho, Jae-Young Kang, Giwon Lee +4
End-to-end autonomous driving (E2E-AD) has emerged as a promising paradigm that unifies perception, prediction, and planning into a holistic, data-driven framework. However, achiev…