Vehicle Autonomy & Intelligence Lab

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, 한국교통안전공단, SUM, 경기도, KakaoMobility.

News
[2026.09] Two new graduate students joined our lab! Welcome, Hyeonji Lee (from KAIST CS/AE) and Seunghoon Lee (from SNU ME)! & Prof. Joa presented at Univ. of Tokyo, 대한교통학회 특별세션 and Hyundai Mobis.
[2026.08] Prof. Joa presented at KSME workshop and IFAC 2026. & was interviewed by Seoul Economic Daily.
[2026.07] Dabin Seo's model ranked 1st (1st in Spotlight scenarios) in Waymo's vision e2e challenge (as of 07/07). & Myeonggeun Jun's IEEE CDC paper has been accepted. & Prof. Joa participated into Autonomous Driving AI Expert Discussion with MOLIT Minister & presented at National Artificial Intelligence Strategy Committee and Hyundai Mobis & was interviewed by Maeil Business.
[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

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

There are no openings for Spring 2027. For Fall 2027, we will post an admissions notice after the Spring 2027 admission results have been announced.

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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