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FROM VISION TO UNDERSTANDING: Leveraging VLMs to enable autonomous driving decisions
Optical Flow with SAM2 and YOLO-V8:
Result
Flowchart
Optical Flow aided Ego-Vehicle Motion Estimation:
Road Segmentation Mask
Largest Rectangle Drivable area
Optical flow of dynamic objects
Optical Flow of drivable are
Ego-Vehicle motion direction visualization - This shows 4 consecutive frame’s motion direction of the ego-vehicle in 3D world coordinates - estimated from 1 camera using Optical Flow
About
This project integrates Vision-Language Models (VLMs) with autonomous driving systems to enhance decision-making through scene understanding and reasoning. Techniques like YOLOv8, SAM2, and optical flow are utilized for robust object tracking and motion estimation.