RoboRacer & Autonomous Driving Research Themes
1. Algorithm Research Based on Small-Scale Autonomous Driving Platform (RoboRacer, 1/10-scale)
We conduct research to scale down and implement real-world autonomous driving algorithms in an experimentally feasible form using RoboRacer (F1TENTH) vehicles.
- SLAM, Localization, Mapping
- Obstacle avoidance (Follow-The-Gap, AEB), Pure Pursuit-based path following
- Optimal trajectory generation and high-speed racing
2. Embodied AI–World Model + Model Checking-based Simulation Safety & Reliability Research
In an Embodied AI environment where autonomous agents interact with the environment to learn and reason, we combine World Model and Formal Verification (Model Checking) to research stable and reliable simulation and driving control.
- Environment dynamics prediction and future state simulation through World Model
- Safety verification of environment and situation models based on Timed Automata
- Integration of safety constraints derived from model checking into RL and planning
- Minimization of risk situations that may occur during sim-to-real transfer
- Enhancement of stability and consistency in scenario generation within simulators
Core Objective:
"Development of a verification-aware Embodied AI autonomous driving framework that ensures learning and control algorithms do not violate safety constraints."
3. Simulator-based Autonomous Driving Learning and Evaluation
To reduce the risks and costs of real vehicle experiments, we repeatedly validate algorithms in Gazebo / Isaac / custom simulator environments.
- RL and imitation learning policy training
- Automatic generation of challenging scenarios and stress-testing
- Continuous learning combined with World Model-based predictive simulation
- Design to minimize sim-to-real performance gap
4. Safe Autonomous Driving with Formal Verification
We systematize safety and reliability assurance of autonomous driving algorithms through formal methods.
- Modeling of driving, collision, and braking scenarios based on Timed Automata
- Model checking of safety properties for collision/braking conditions
- Design of verification-guided control and runtime shielding
- Automatic defensive action control for safety non-compliance situations
5. Application of Lightweight AI / World Models to Autonomous Driving
We lightweight LLM and World Model technologies and implement them to meet real-time requirements for autonomous driving environments.
- Environment prediction and future trajectory sampling based on World Model
- Planning and control considering uncertainty
- Multi-modal state representation learning
6. Building an Education and Open-Source-based Autonomous Driving Ecosystem
We build an education–research–competition ecosystem centered around RoboRacer Korea.
- Development of ROS2-based lab assignments and practice materials (Lab-06~Lab-12)
- Operation of beginner to advanced workshops and real vehicle practice-based educational curriculum
- Activation of nationwide RoboRacer network and open-source projects