3 papers
cs.AI2026
GuidedWAM: Training-Free Energy Guidance for World Action Models in Autonomous Driving
Xiyan Su, Finn Rasmus Schäfer, Hojun Lim +3
World action models (WAMs) for autonomous driving couple the driving policy and the visual world model in a single generative network whose objectives are fixed post-training. Cond…
cs.CV2026
Digital Twin-Driven Real2Sim2Real: Simulator-Conditioned Generation via Paired Driving-Scene Reconstruction
Hojun Lim, Hyeongseok Jeon, Donghyun Kim +2
Camera-based 3D perception for autonomous driving relies heavily on large annotated datasets, and deploying such a system to a new target region typically requires data collection…
cs.CV2025
MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain
Hojun Lim, Heecheol Yoo, Jinwoo Lee +2
Deep neural network (DNN) based perception models are indispensable in the development of autonomous vehicles (AVs). However, their reliance on large-scale, high-quality data is br…