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AI and ML research for mobility data, vehicles, and robotics.
AI and ML research for mobility data, vehicles, and robotics.
Lab Equipment
Currently, four GPU servers (equipped with A5000/RTX 3090TI-level GPUs x 6EA each)
H100 GPUs are expected to be ready by late 2025.
Data server
Sensors and Dataset:
LiDAR (Ouster OS1)
RGB Machine Camera (IDS Camera)
Special dataset integrating radar, LiDAR, and camera vehicle sensors.
Robots
Unmanned Ground Vehicle ( Jakal-level size robot) x 2
1/5-level scale car (Mobility Embedded System Design)
MORAI autonomous driving simulator available.
1. Perception in self-driving vehicles
- Multimodal Learning for Sensor Fusion
- Multi-modal Object detection(using LIDAR, camera, radar and sound sensors)
- Radar-Camera sensor fusion
- LIDAR-Camera sensor fusion
- Deep learning-based 3D object detection
- Sensor's for Self-driving environment
- Calibration methods for sensors
- Sensor configuration optimization
- Higher attribute recognition with Visual Language model
- Intention estimation
- Mobility/Traffic rare event detection
2. Spatial AI
- Gaussian Splatting & Neural radiance field (NeRF)
- Vehicle Localization (Vehicle pose estimation)
- SLAM (Simultaneous Localization and Mapping)
- Place recognition
- Non-line of Sight(Invisible) Object Localization
3. Dataset generation and improvement for self-driving vehicle and mobility applications
- Domain adaptation
- Knowledge distillation
- Test-time adaptation
- Generative models
4. Path planning and Trajectory Estimation
- Object Trajectory Estimation
- Stop and Go problems
- Lane change problems
- Pedestrian vague crossing problems
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