Autonomous Driving AI Chip Design Baseline File Structure

2026 Autonomous Driving AI Chip Design for Sungkyunkwan University Students

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Related Repositories & Resources

• YOLACT_SKKU
• yolact_vitis_ai_SKKU
• Tiny-yolov3-keras
• ros2_autonomous_vehicle_simulation
• PWM RTL Code (Google Drive)
• Test dataset (Google Drive)
• YOLOv3-tiny Vitis-ai (Google Drive)
• Educational Material (Google Drive)

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Folder Structure

$\textcolor{red}{\mathbf{(NEW)}}$ Segmentation

• test_data
  Inference test images
• dpu_yolact.ipynb
  Jupyter Notebook for real-time segmentation inference (~20FPS)

debugging

• SoC_Driving.ipynb
  Jupyter Notebook for driving code
• data_collection.ipynb
  Jupyter Notebook for data collection
• $\textcolor{red}{\mathbf{(NEW)}}$ test_sonic.ipynb
  Jupyter Notebook for Ultrasonic sensor test (for reference only, simple example)

dpu

• dpu.bit
  DPU bitstream file (Students need to add)
• dpu.hwh
  DPU hardware file (Students need to add)
• dpu.xclbin
  DPU executable file (Students need to add)

driving

• config.py
  Initial motor address settings
• driving_system_controller.py
  Driving mode settings
• image_processor.py
  Image processing script
• main.py
  Driving parameter settings
• motor_controller.py
  Motor control settings
• yolo_utils.py
  YOLO utility functions

test_video

• test_video.mp4
  Test video file

xmodel

• lane_class.txt
  Model class configuration (Students need to add)
• top-tiny-yolov3_coco_256.xmodel
  Compiled deep learning model file (Students need to add)

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