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Driving Like a Human: Imitation Learning for Path Planning using CNNs Eike Rehder, Jannik Quehl and Christoph Stiller eike.rehder@daimler.com IROS 2017 | 24.09.2017 KIT The Research University in the Helmholtz Association www.kit.edu

  1. Driving Like a Human: Imitation Learning for Path Planning using CNNs Eike Rehder, Jannik Quehl and Christoph Stiller eike.rehder@daimler.com IROS 2017 | 24.09.2017 KIT – The Research University in the Helmholtz Association www.kit.edu

  2. Introduction: Path Planning 2 Eike Rehder | IROS 2017 | 24.09.2017

  3. Short Review: Dijkstra‘s Algorithm 3 Eike Rehder | IROS 2017 | 24.09.2017

  4. Short Review: Dijkstra‘s Algorithm Find shortest path from start to goal: Start Goal 4 Eike Rehder | IROS 2017 | 24.09.2017

  5. Short Review: Dijkstra‘s Algorithm Find shortest path from start to goal: 1 ? 4 ? 2 0 4 ? Start 4 Goal 2 2 ? ? 5 Assign edge costs, node costs, Start = 0 5 Eike Rehder | IROS 2017 | 24.09.2017

  6. Short Review: Dijkstra‘s Algorithm Find shortest path from start to goal: 1 4 4 ? 2 0 4 ? Start 4 Goal 2 2 ? 2 5 Assign edge costs, node costs, Start = 0 Propagate and sum costs 6 Eike Rehder | IROS 2017 | 24.09.2017

  7. Short Review: Dijkstra‘s Algorithm Find shortest path from start to goal: 1 4 4 6 2 0 4 ? Start 4 Goal 2 2 7 2 5 Assign edge costs, node costs, Start = 0 Propagate and sum costs Expand cheapest node 7 Eike Rehder | IROS 2017 | 24.09.2017

  8. Short Review: Dijkstra‘s Algorithm Find shortest path from start to goal: 1 4 4 6 2 0 4 ? Start 4 Goal 2 2 7 2 5 Assign edge costs, node costs, Start = 0 Propagate and sum costs Expand cheapest node 8 Eike Rehder | IROS 2017 | 24.09.2017

  9. Short Review: Dijkstra‘s Algorithm Find shortest path from start to goal: 1 4 4 6 2 0 4 ? Start 4 Goal 2 2 7 2 5 Assign edge costs, node costs, Start = 0 Propagate and sum costs Expand cheapest node 9 Eike Rehder | IROS 2017 | 24.09.2017

  10. Short Review: Dijkstra‘s Algorithm Find shortest path from start to goal: 1 4 4 5 2 0 4 ? Start 4 Goal 2 2 7 2 5 Assign edge costs, node costs, Start = 0 Propagate and sum costs Expand cheapest node Re-assign minimum cost 10 Eike Rehder | IROS 2017 | 24.09.2017

  11. Short Review: Dijkstra‘s Algorithm Find shortest path from start to goal: 1 4 4 5 2 0 4 7 Start 4 Goal 2 2 7 2 5 Assign edge costs, node costs, Start = 0 Propagate and sum costs Expand cheapest node Re-assign minimum cost 11 Eike Rehder | IROS 2017 | 24.09.2017

  12. Short Review: Dijkstra‘s Algorithm Find shortest path from start to goal: 1 4 4 5 2 0 4 7 Start 4 Goal 2 2 7 2 5 Assign edge costs, node costs, Start = 0 Propagate and sum costs Expand cheapest node Re-assign minimum cost Trace back shortest path 12 Eike Rehder | IROS 2017 | 24.09.2017

  13. Short Review: Dijkstra‘s Algorithm Find shortest path from start to goal: Goal Start 13 Eike Rehder | IROS 2017 | 24.09.2017

  14. Short Review: Dijkstra‘s Algorithm Find shortest path from start to goal: Graph Edges 14 Eike Rehder | IROS 2017 | 24.09.2017

  15. Short Review: Dijkstra‘s Algorithm Find shortest path from start to goal: Obstacle 15 Eike Rehder | IROS 2017 | 24.09.2017

  16. Short Review: Dijkstra‘s Algorithm Find shortest path from start to goal: 16 Eike Rehder | IROS 2017 | 24.09.2017

  17. Shortest Path with a CNN 17 Eike Rehder | IROS 2017 | 24.09.2017

  18. Finding the Shortest Path with a CNN Assign edge costs, node costs, Start = 0 18 Eike Rehder | IROS 2017 | 24.09.2017

  19. Finding the Shortest Path with a CNN Assign edge costs, node costs, Start = 0 Propagate 19 Eike Rehder | IROS 2017 | 24.09.2017

  20. Finding the Shortest Path with a CNN Assign edge costs, node costs, Start = 0 Propagate = 20 Eike Rehder | IROS 2017 | 24.09.2017

  21. Finding the Shortest Path with a CNN Assign edge costs, node costs, Start = 0 Propagate and sum costs = + 1 = 21 Eike Rehder | IROS 2017 | 24.09.2017

  22. Finding the Shortest Path with a CNN Assign edge costs, node costs, Start = 0 Propagate and sum costs +1 +1 +1 = +1 +0 22 Eike Rehder | IROS 2017 | 24.09.2017

  23. Finding the Shortest Path with a CNN Assign edge costs, node costs, Start = 0 Propagate and sum costs = 23 Eike Rehder | IROS 2017 | 24.09.2017

  24. Finding the Shortest Path with a CNN Assign edge costs, node costs, Start = 0 Propagate and sum costs Re-assign minimum cost min = 24 Eike Rehder | IROS 2017 | 24.09.2017

  25. Finding the Shortest Path with a CNN Assign edge costs, node costs, Start = 0 Propagate and sum costs Re-assign minimum cost min = 25 Eike Rehder | IROS 2017 | 24.09.2017

  26. Finding the Shortest Path with a CNN Replace min + pool Transition Filters Transition Cost Cost Updated Non-Zero Cost per Cost Padding (!) Action “Reinforcement learning via recurrent convolutional neural networks”, arXiv:1701.02392 26 Eike Rehder | IROS 2017 | 24.09.2017

  27. Finding the Shortest Path with a CNN Replace min + pool Transition Filters Transition Cost Cost Updated Non-Zero Cost per Cost Padding (!) Action Argmin of this layer is transition policy 27 Eike Rehder | IROS 2017 | 24.09.2017

  28. Evaluating the Shortest Path with a CNN + argmin * Flipped Transition Filters Current Transition Transition Next Destination State Policy Selection State State 28 Eike Rehder | IROS 2017 | 24.09.2017

  29. Example: Simple Path Planning 29 Eike Rehder | IROS 2017 | 24.09.2017

  30. Example: Path Planning Find shortest path from start to goal: Goal Obstacle Start 30 Eike Rehder | IROS 2017 | 24.09.2017

  31. Example: Path Planning Nine possible transition filters Cost is the traversed distance +√2 +√2 +1 +1 +0 +1 +√2 +√2 +1 31 Eike Rehder | IROS 2017 | 24.09.2017

  32. Example: Path Planning Cost Model Additive layer High cost where obstacle is located 32 Eike Rehder | IROS 2017 | 24.09.2017

  33. Example: Path Planning Cost Map State Transition Map 33 Eike Rehder | IROS 2017 | 24.09.2017

  34. Finding the Shortest Path with a CNN If you use Dijkstra: Graph traversal with known transitions is faster States can be updated selectively Visited nodes will never be touched again Why would you do it then? 34 Eike Rehder | IROS 2017 | 24.09.2017

  35. Driving Like a Human: Imitation Learning 35 Eike Rehder | IROS 2017 | 24.09.2017

  36. Imitation Learning Intersection in Karlsruhe Arial view: Google Maps 36 Eike Rehder | IROS 2017 | 24.09.2017

  37. Imitation Learning Recorded trajectories Teach a network to imitate human behavior Intersection in Karlsruhe Arial view: Google Maps 37 Eike Rehder | IROS 2017 | 24.09.2017

  38. Imitation Learning Replace min + pool Transition Filters Transition Cost Cost Updated Non-Zero Cost per Cost Padding (!) Action 38 Eike Rehder | IROS 2017 | 24.09.2017

  39. Imitation Learning Replace min + pool Transition Filters Transition Cost Cost Updated Non-Zero Cost per Cost Padding (!) Action Fill in the whole bunch of CNN techniques 39 Eike Rehder | IROS 2017 | 24.09.2017

  40. Example II: Imitation Learning + In our case: FC-ResNet operating on the arial view 40 Eike Rehder | IROS 2017 | 24.09.2017

  41. Imitation Learning Path driven by human 41 Eike Rehder | IROS 2017 | 24.09.2017

  42. Imitation Learning Path driven by human Cost map from arial image 42 Eike Rehder | IROS 2017 | 24.09.2017

  43. Imitation Learning Path driven by human Cost map after planning 43 Eike Rehder | IROS 2017 | 24.09.2017

  44. Imitation Learning Path planned by network Cost map after planning 44 Eike Rehder | IROS 2017 | 24.09.2017

  45. Imitation Learning Path planned by network Cost map after planning Path driven by human 45 Eike Rehder | IROS 2017 | 24.09.2017

  46. Imitation Learning Path planned by network Path driven by human 46 Eike Rehder | IROS 2017 | 24.09.2017

  47. Outlook: Prediction and Cooperation 47 Eike Rehder | IROS 2017 | 24.09.2017

  48. Outlook: Pedestrian Prediction Camera image Road Sidewalk Obstacles Semantic map and top view Teach a network to predict human motion by planning 48 Eike Rehder | IROS 2017 | 24.09.2017

  49. Outlook: Pedestrian Prediction Road Sidewalk Obstacles Pedestrian Crop of map centered around pedestrian “Pedestrian Prediction by Planning using Deep Neural Networks”, arXiv:1706.05904 49 Eike Rehder | IROS 2017 | 24.09.2017

  50. Outlook: Pedestrian Prediction Road Sidewalk Obstacles Pedestrian Predict destination for planning “Pedestrian Prediction by Planning using Deep Neural Networks”, arXiv:1706.05904 50 Eike Rehder | IROS 2017 | 24.09.2017

  51. Outlook: Pedestrian Prediction Road Sidewalk Obstacles Pedestrian Predicted with Net “Pedestrian Prediction by Planning using Deep Neural Networks”, arXiv:1706.05904 51 Eike Rehder | IROS 2017 | 24.09.2017

  52. Outlook: Cooperative Planning “ Cooperative Motion Planning for Non-Holonomic Agents Teach a network resolve conflicts with Value Iteration Networks ”, arXiv:1706.05904 52 Eike Rehder | IROS 2017 | 24.09.2017

  53. Summary 53 Eike Rehder | IROS 2017 | 24.09.2017

  54. Summary Planning Net… … for imitation … for prediction … for cooperation 54 Eike Rehder | IROS 2017 | 24.09.2017

  55. The People Jannik Quehl Trajectory Data Maximilian Naumann Cooperative Planning Florian Wirth Destination Prediction 55 Eike Rehder | IROS 2017 | 24.09.2017

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