Giwon Lee

KAIST VILAB. 1st Year Ph.D. student

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Hello! I am a first-year Ph.D. student in VILAB led by Prof. Kuk-Jin Yoon in the Department of Mechanical Engineering at KAIST. My research spans the full spectrum of planning — from high-level reasoning to low-level control — with the long-term goal of building a single, coherent framework that can be entrusted to act fully autonomously, end to end.

I think of this stack in three layers. At the high level, Agentic AI handles reasoning, decision-making, and long-horizon planning. As the bridge, Vision-Language-Action (VLA) and Vision-Language Navigation (VLN) models ground language and perception into embodied action. At the low level, Motion Planning and End-to-End Autonomous Driving turn intent into safe, executable behavior. My earlier research centered on this low level, primarily in autonomous driving; my current work moves up the stack to the high and bridge levels — these days on Agentic AI, VLA, and VLN — building toward connecting all three into one autonomous whole.

I am also eager to gain hands-on industry experience through internships, and I would be grateful to hear about any relevant opportunities. Please feel free to get in touch by email or on LinkedIn.

news

Jun 18, 2026 Two papers, “Unified Prediction and Planning via Conflict-Aware Disjoint Parameter Training” and “VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation,” have been accepted to ECCV 2026.
Sep 18, 2025 “VR-Drive: Viewpoint-Robust End-to-End Driving with Feed-Forward 3D Gaussian Splatting,” has been accepted to NeurIPS 2025.

selected publications

  1. ECCV2026_DPT.jpg
    Unified Prediction and Planning via Conflict-Aware Disjoint Parameter Training
    Taewon Seo*, Seonae Jeon*, Giwon Lee*, Kuk-Jin Yoon, and Daehee Park
    European Conference on Computer Vision (ECCV), 2026
  2. ECCV2026_VIPS.png
    VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation
    Hoonhee Cho*, Jae-Young Kang*, Giwon Lee*, Hyemin Yang*, Heejun Park*, and Kuk-Jin Yoon
    European Conference on Computer Vision (ECCV), 2026
  3. Archieve_HEAT.jpg
    HEAT: Heterogeneous End-to-End Autonomous Driving via Trajectory-Guided World Models
    Hoonhee Cho*, Giwon Lee*, Jae-Young Kang*, Hyemin Yang*, Heejun Park*, and Kuk-Jin Yoon
    arXiv preprint arXiv:2605.19631, 2026
  4. NeurIPS2025.png
    VR-Drive: Viewpoint-Robust End-to-End Driving with Feed-Forward 3D Gaussian Splatting
    Hoonhee Cho*, Jae-Young Kang*, Giwon Lee*, Hyemin Yang*, Heejun Park, Seokwoo Jung, and Kuk-Jin Yoon
    Conference on Neural Information Processing Systems (NeurIPS), 2025
  5. ICCV2025.png
    Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning
    Giwon Lee*, Wooseong Jeong*, Daehee Park, Jaewoo Jeong, and Kuk-Jin Yoon
    IEEE/CVF International Conference on Computer Vision (ICCV), 2025
    ⭐ Highlight ⭐
  6. IROS2025.png
    Non-differentiable Reward Optimization for Diffusion-based Autonomous Motion Planning
    Giwon Lee*, Daehee Park*, Jaewoo Jeong*, and Kuk-Jin Yoon
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025
  7. CVPR2025.png
    Multi-modal Knowledge Distillation-based Human Trajectory Forecasting
    Jaewoo Jeong, Seohee Lee, Daehee Park, Giwon Lee, and Kuk-Jin Yoon
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025