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About Us
About Us
Vehicle Autonomy and Intelligence Lab (VAIL, 자율주행 및 차량 지능 연구실)은 자율주행 시스템과 차량 제어 시스템이 (1) 실제 차량에서 안전하게 작동하고, (2) 새로운 도로와 도시로 확장가능하며, (3) 에너지 효율적인 것을 목표로 합니다.
We focus on developing theoretical and algorithmic methods to address fundamental challenges in vehicle autonomy and the physical intelligence in vehicles and mobile robots, with the goal of enabling safer, human-centric, and energy-efficient mobility at scale.
Research fields:
(1) Vehicle Autonomy: Closed-loop Behavior and Motion Planning with real-vehicle validation.
(2) Vision-Language-Action (VLA) models for Decision Making.
(3) Vehicle Dynamics and Control: Chassis Control, Vehicle Control at the Limits
(4) Mobile robotics
Please refer to the lab poster (link) for more details!
Sponsored by:
Hyundai Motor Company, SUM, 경기도.
News
[2026.07] Dabin Seo's model ranks 1st (1st in Spotlight scenarios) in Waymo's vision e2e challenge (07/07). & Prof. Joa participated into Autonomous Driving AI Expert Discussion with MOLIT Minister & presented at National Artificial Intelligence Strategy Committee.
[2026.06] Prof. Joa presented at Hyundai Mobis, IEEE IV 2026 workshop, and KIEV workshop.
[2026.03] Vehicle Autonomy & Intelligence Lab officially launched at SNU. & Nine Ph.D. students, formerly advised by the late Prof. Kyongsu Yi, have joined the lab. & Prof. Joa presented at Kakao Mobility.
[2026.02] Dr. Eunhyek Joa presented at the FMTC Tech Forum - K-Autonomous Driving Tech Day.
Prospective Students
Prospective Students
Please send your CV and a brief statement of purpose to eunhyekj[at]snu.ac.kr
We recruit on a rolling basis: if you meet our bar, you will receive an offer, even if stronger applicants may apply later.
2027 admission cycle
we are recruiting up to two additional graduate students. (Four students have already been tentatively appointed.)
Strong candidates will have a background in robotics, mathematics, deep learning, or reinforcement learning, along with proficiency in Python.
Research experience, such as a first-authored paper or substantial project experience, is preferred. Internship experience at a software company or tech company, as well as practical experience with PyTorch, JAX, or other machine learning tools, is also considered a plus.
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