Recent research in autonomous driving generally follows two major directions. The first is a Data-Driven approach, like Full Self-Driving (FSD), which relies on massive datasets. The second approach utilizes HD Maps.
While the Data-Driven method achieves high performance by heavily relying on real driving data, it requires immense resources and time for data collection and training, and faces limitations in generalization to new environments. In contrast, the HD Map-based method allows for stable and accurate path planning, but involves very high costs for map construction and maintenance, and cannot operate in unmapped areas.
Our lab proposes and researches a ‘Data Efficient Light-weight Autonomous Driving’ approach, which aims to leverage the strengths of both methods while compensating for their weaknesses.
Our research focuses on the following three core elements:
The table below summarizes our primary research areas, goals, and features.
| Research Area | Goal | Feature |
|---|---|---|
| Perception | Accurate environment recognition with less data | Object/Road Recognition based on Self-Supervised Learning |
| Localization | Precise position estimation using public maps and sensor data | Light-weight Localization Technology integrating GNSS/IMU and map data |
| Planning | Safe and smooth path generation with limited information | Driving Strategy establishment based on Light-weight Model considering prediction uncertainty |
Background: To develop robust autonomous driving systems by AI-based perception.
Key Research: Developing reliable and efficient perception methods, including Object Detection, Lane Segmentation, and Occupancy Prediction, to accurately understand the surrounding environment from sensor data.
Background: Ensuring autonomous driving stability through precise localization using public maps and SLAM in situations where RTK-GPS and HD Maps are limited.
Key Research: Researching precise localization methods combining publicly available map information (e.g., OpenStreetMap, commercial navigation maps) and SLAM (Simultaneous Localization and Mapping) technology.
Associated Papers & Demonstrations
Background: Aiming to achieve final planning based on hierarchical information obtained through Localization and Perception.
Key Research: Researching human-like planning methods in diverse and complex environments, guided by public map sources.