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Deep Learning for Automated Systems: From the Warehouse to the Road Dr. Melissa C. Smith Clemson University Future Computing Technologies Laboratory Eddie Weill, Sufeng Niu, Colin Targonski, and Ben Shealy Overview Simulation Using deep


  1. Deep Learning for Automated Systems: From the Warehouse to the Road Dr. Melissa C. Smith Clemson University Future Computing Technologies Laboratory Eddie Weill, Sufeng Niu, Colin Targonski, and Ben Shealy

  2. Overview

  3. Simulation Using deep learning in a simulated environment

  4. The Need for a Simulator

  5. Deep Learning in a Simulator Webpage: http://www.carla.org/ Paper: Dosovitskiy, Alexey, et al. "CARLA: An open urban driving simulator." arXiv preprint arXiv:1711.03938 (2017).

  6. Perception Developing perception for automated systems

  7. Autonomous Driving Perception

  8. Autonomous Driving Perception

  9. Autonomous Driving Perception Tetreault, Jesse. Deep Multimodal Fusion Networks for Semantic Segmentation . Diss. Clemson University, 2017.

  10. Perception on Embedded Devices Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." Synthetic Image Proceedings of the IEEE conference on computer vision and pattern recognition . 2016. Training Detector OCR Detections Network (i.e. Extracted Text Extraction YOLO, SSD) Acknowledgement goes to BMW ITRC for partnering on this endeavor and providing the data for experimentation Luckow, Andre, et al. "Deep learning in the automotive industry: Applications and tools." Big Data (Big Data), 2016 IEEE International Conference on . IEEE, 2016.

  11. Perception + Control Integrating perception with reinforcement learning

  12. Autonomous Driving in CARLA Action (Steering / Acceleration) Combine Feature Maps Rewards Reinforcement Learning Agent Environment “Approaching Stop Sign” (Carla) “Light is Yellow” “Car stopped at intersection” State (RGB Image) Segmentation Traffic Interpretation Object Detection

  13. Autonomous Driving in CARLA

  14. Planning Using reinforcement learning to explore environments

  15. Neural Network Based Planning VIN GVIN 16 x 16 2D Maze New York City Street Map (13K intersections) A. Tamar, et al. Value Iteration Networks . NIPS, 2016. S. Niu, et al. Generalized Value Iteration Network: Life Beyond Lattices . 32nd AAAI, 2018.

  16. Neural Network Based Planning G S Social network reasoning Navigation Knowledge querying Network routing

  17. Neural Network Based Planning (GVIN) Start Goal

  18. Neural Network Based Planning (GVIN) Testing Training Testing 10 nodes 2642 nodes 5069 nodes S. Niu, et al. Generalized Value Iteration Network: Life Beyond Lattices . 32nd AAAI, 2018.

  19. Distributed Computing Scaling beyond a single HPC cluster

  20. Clemson University’s Palmetto Cluster

  21. Is One HPC Cluster Enough? www.nlm.nih.gov Image credit: http://harborresearch.com/connected-vehicles-rise-transportation-ecosystems/

  22. Scientific Data Analysis at Scale (SciDAS) NSF Award No. 1659300

  23. Scientific Data Analysis at Scale (SciDAS) NSF Award No. 1659300

  24. Scientific Data Analysis at Scale (SciDAS) NSF Award No. 1659300

  25. Scientific Data Analysis at Scale (SciDAS) NSF Award No. 1659300

  26. Scientific Data Analysis at Scale (SciDAS) NSF Award No. 1659300

  27. Wrap Up

  28. Thank you! Questions?

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