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Vehicle Trajectory Prediction in Crowded Highway Scenarios Using - PowerPoint PPT Presentation

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IEEE Intelligent Transportation Systems Conference - ITSC 2020 Vehicle Trajectory Prediction in Crowded Highway Scenarios Using Bird Eye View Representations and CNNs R. Izquierdo, A. Quintanar I. Parra, D. Fernndez-Llorca and M. A. Sotelo

  1. Dataset 6 HighD Dataset 1 • Publicy available at highd-dataset.com HighD Challenge • Road Structure Samples 39M 1500K • Unique IDs Vehicles 110K 110K Hours 147 16 • Vehicle dimension Clips 60 60 • Position, Speed, and Acceleration at 25 Hz 1 The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems. Krajewski, Robert and Bock, Julian and Kloeker, Laurent and Eckstein, Lutz R. Izquierdo | ITSC 2020, Virtual Conference

  2. Dataset 6 HighD Dataset 1 • Publicy available at highd-dataset.com HighD Challenge • Road Structure Samples 39M 1500K • Unique IDs Vehicles 110K 110K Hours 147 16 • Vehicle dimension Clips 60 60 • Position, Speed, and Acceleration at 25 Hz 1 The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems. Krajewski, Robert and Bock, Julian and Kloeker, Laurent and Eckstein, Lutz R. Izquierdo | ITSC 2020, Virtual Conference

  3. Dataset 6 HighD Dataset 1 • Publicy available at highd-dataset.com HighD Challenge • Road Structure Samples 39M 1500K • Unique IDs Vehicles 110K 110K Hours 147 16 • Vehicle dimension Clips 60 60 • Position, Speed, and Acceleration at 25 Hz 1 The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems. Krajewski, Robert and Bock, Julian and Kloeker, Laurent and Eckstein, Lutz R. Izquierdo | ITSC 2020, Virtual Conference

  4. Dataset 7 Data Codification • Driving Area as an Image: 32x512 m. → 64x512 px. • Vehicles as N ( µ, σ 2 ) • Possible Overlap P ( x , y ) = max {N i } ∀ i • Only Predictable Vehicles R. Izquierdo | ITSC 2020, Virtual Conference

  5. Dataset 7 Data Codification • Driving Area as an Image: 32x512 m. → 64x512 px. • Vehicles as N ( µ, σ 2 ) • Possible Overlap P ( x , y ) = max {N i } ∀ i • Only Predictable Vehicles R. Izquierdo | ITSC 2020, Virtual Conference

  6. Dataset 7 Data Codification • Driving Area as an Image: 32x512 m. → 64x512 px. • Vehicles as N ( µ, σ 2 ) • Possible Overlap P ( x , y ) = max {N i } ∀ i • Only Predictable Vehicles R. Izquierdo | ITSC 2020, Virtual Conference

  7. Dataset 7 Data Codification • Driving Area as an Image: 32x512 m. → 64x512 px. • Vehicles as N ( µ, σ 2 ) • Possible Overlap P ( x , y ) = max {N i } ∀ i • Only Predictable Vehicles R. Izquierdo | ITSC 2020, Virtual Conference

  8. Dataset 7 Data Codification • Driving Area as an Image: 32x512 m. → 64x512 px. • Vehicles as N ( µ, σ 2 ) • Possible Overlap P ( x , y ) = max {N i } ∀ i • Only Predictable Vehicles R. Izquierdo | ITSC 2020, Virtual Conference

  9. Dataset 8 Data Decodification • Iterative Extraction of Vehicle Positions • Search the pixel with the higest probability • Compute the mass center • Reset the area • Vehicle association by Euclidean distance R. Izquierdo | ITSC 2020, Virtual Conference

  10. Dataset 8 Data Decodification • Iterative Extraction of Vehicle Positions • Search the pixel with the higest probability • Compute the mass center • Reset the area • Vehicle association by Euclidean distance R. Izquierdo | ITSC 2020, Virtual Conference

  11. Dataset 8 Data Decodification • Iterative Extraction of Vehicle Positions • Search the pixel with the higest probability • Compute the mass center • Reset the area • Vehicle association by Euclidean distance R. Izquierdo | ITSC 2020, Virtual Conference

  12. Dataset 8 Data Decodification • Iterative Extraction of Vehicle Positions • Search the pixel with the higest probability • Compute the mass center • Reset the area • Vehicle association by Euclidean distance R. Izquierdo | ITSC 2020, Virtual Conference

  13. Dataset 8 Data Decodification • Iterative Extraction of Vehicle Positions • Search the pixel with the higest probability • Compute the mass center • Reset the area • Vehicle association by Euclidean distance R. Izquierdo | ITSC 2020, Virtual Conference

  14. Dataset 8 Data Decodification • Iterative Extraction of Vehicle Positions • Search the pixel with the higest probability • Compute the mass center • Reset the area • Vehicle association by Euclidean distance R. Izquierdo | ITSC 2020, Virtual Conference

  15. Dataset 8 Data Decodification • Iterative Extraction of Vehicle Positions • Search the pixel with the higest probability • Compute the mass center • Reset the area • Vehicle association by Euclidean distance R. Izquierdo | ITSC 2020, Virtual Conference

  16. Dataset 8 Data Decodification • Iterative Extraction of Vehicle Positions • Search the pixel with the higest probability • Compute the mass center • Reset the area • Vehicle association by Euclidean distance R. Izquierdo | ITSC 2020, Virtual Conference

  17. Dataset 8 Data Decodification • Iterative Extraction of Vehicle Positions • Search the pixel with the higest probability • Compute the mass center • Reset the area • Vehicle association by Euclidean distance R. Izquierdo | ITSC 2020, Virtual Conference

  18. Dataset 8 Data Decodification • Iterative Extraction of Vehicle Positions • Search the pixel with the higest probability • Compute the mass center • Reset the area • Vehicle association by Euclidean distance R. Izquierdo | ITSC 2020, Virtual Conference

  19. Outline 9 Motivation Dataset Network Architecture Results Conclusions & Future Work R. Izquierdo | ITSC 2020, Virtual Conference

  20. Network Architecture 10 U-net model Depth levels 4 5 6 7 • Number of filters. Receptive Field ± 76 ± 156 ± 316 ± 636 Input size 16 32 64 128 • Depth levels. Parameters 56k 116k 235k 472k R. Izquierdo | ITSC 2020, Virtual Conference

  21. Network Architecture 10 U-net model Depth levels 4 5 6 7 • Number of filters. Receptive Field ± 76 ± 156 ± 316 ± 636 Input size 16 32 64 128 • Depth levels. Parameters 56k 116k 235k 472k R. Izquierdo | ITSC 2020, Virtual Conference

  22. Network Architecture 10 U-net model Depth levels 4 5 6 7 • Number of filters. Receptive Field ± 76 ± 156 ± 316 ± 636 Input size 16 32 64 128 • Depth levels. Parameters 56k 116k 235k 472k R. Izquierdo | ITSC 2020, Virtual Conference

  23. Network Architecture 10 U-net model Depth levels 4 5 6 7 • Number of filters. Receptive Field ± 76 ± 156 ± 316 ± 636 Input size 16 32 64 128 • Depth levels. Parameters 56k 116k 235k 472k R. Izquierdo | ITSC 2020, Virtual Conference

  24. Network Architecture 10 U-net model Depth levels 4 5 6 7 • Number of filters. Receptive Field ± 76 ± 156 ± 316 ± 636 Input size 16 32 64 128 • Depth levels. Parameters 56k 116k 235k 472k R. Izquierdo | ITSC 2020, Virtual Conference

  25. Network Architecture 10 U-net model Depth levels 4 5 6 7 • Number of filters. Receptive Field ± 76 ± 156 ± 316 ± 636 Input size 16 32 64 128 • Depth levels. Parameters 56k 116k 235k 472k R. Izquierdo | ITSC 2020, Virtual Conference

  26. Network Architecture 10 U-net model Depth levels 4 5 6 7 • Number of filters. Receptive Field ± 76 ± 156 ± 316 ± 636 Input size 16 32 64 128 • Depth levels. Parameters 56k 116k 235k 472k R. Izquierdo | ITSC 2020, Virtual Conference

  27. Network Architecture 11 Problem Application • Data rate lowered to 5 Hz • Input & Output 15@64x512 BEV − 2 . 8 ≤ t ≤ 3 . 0 • Activation Layer: • Linear • Clipped Rect. Linear Unit • Hyperbolic tangent • Loss function SE R. Izquierdo | ITSC 2020, Virtual Conference

  28. Network Architecture 11 Problem Application • Data rate lowered to 5 Hz • Input & Output 15@64x512 BEV − 2 . 8 ≤ t ≤ 3 . 0 • Activation Layer: • Linear • Clipped Rect. Linear Unit • Hyperbolic tangent • Loss function SE R. Izquierdo | ITSC 2020, Virtual Conference

  29. Network Architecture 11 Problem Application • Data rate lowered to 5 Hz • Input & Output 15@64x512 BEV − 2 . 8 ≤ t ≤ 3 . 0 • Activation Layer: • Linear • Clipped Rect. Linear Unit • Hyperbolic tangent • Loss function SE R. Izquierdo | ITSC 2020, Virtual Conference

  30. Network Architecture 11 Problem Application • Data rate lowered to 5 Hz • Input & Output 15@64x512 BEV − 2 . 8 ≤ t ≤ 3 . 0 • Activation Layer: • Linear • Clipped Rect. Linear Unit • Hyperbolic tangent • Loss function SE R. Izquierdo | ITSC 2020, Virtual Conference

  31. Network Architecture 11 Problem Application • Data rate lowered to 5 Hz • Input & Output 15@64x512 BEV − 2 . 8 ≤ t ≤ 3 . 0 • Activation Layer: • Linear • Clipped Rect. Linear Unit • Hyperbolic tangent • Loss function SE R. Izquierdo | ITSC 2020, Virtual Conference

  32. Network Architecture 11 Problem Application • Data rate lowered to 5 Hz • Input & Output 15@64x512 BEV − 2 . 8 ≤ t ≤ 3 . 0 • Activation Layer: • Linear • Clipped Rect. Linear Unit • Hyperbolic tangent • Loss function SE R. Izquierdo | ITSC 2020, Virtual Conference

  33. Network Architecture 11 Problem Application • Data rate lowered to 5 Hz • Input & Output 15@64x512 BEV − 2 . 8 ≤ t ≤ 3 . 0 • Activation Layer: • Linear • Clipped Rect. Linear Unit • Hyperbolic tangent • Loss function SE R. Izquierdo | ITSC 2020, Virtual Conference

  34. Network Architecture 11 Problem Application • Data rate lowered to 5 Hz • Input & Output 15@64x512 BEV − 2 . 8 ≤ t ≤ 3 . 0 • Activation Layer: • Linear • Clipped Rect. Linear Unit • Hyperbolic tangent • Loss function SE R. Izquierdo | ITSC 2020, Virtual Conference

  35. Network Architecture 11 Problem Application • Data rate lowered to 5 Hz • Input & Output 15@64x512 BEV − 2 . 8 ≤ t ≤ 3 . 0 • Activation Layer: • Linear • Clipped Rect. Linear Unit • Hyperbolic tangent • Loss function SE R. Izquierdo | ITSC 2020, Virtual Conference

  36. Outline 12 Motivation Dataset Network Architecture Results Conclusions & Future Work R. Izquierdo | ITSC 2020, Virtual Conference

  37. Results 13 Training setup • Seq 1-20 28K samples t = 0 . 2 t = 3 . 0 • Mini-batch size 1 Model ε x / ε y ε x / ε y • Epoch 1 Const. acc. 0.02 / 0.00 0.23 / 0.17 d = 5, f = linear 0.52 / 0.17 2.36 / 0.54 • Learning rate 10 − 6 d = 6, f = linear 0.23 / 0.01 1.23 / 0.07 • Momentum 0.9 d = 5, f = tanh - / - - / - • Loss function SE d = 6, f = tanh - / - - / - d = 5, f = cRelu 0.74 / 0.38 2.51 / 0.94 Test Results d = 6, f = cRelu 0.46 / 0.22 2.06 / 0.62 • Seq 21-25 7K samples • Position MAE R. Izquierdo | ITSC 2020, Virtual Conference

  38. Results 13 Training setup • Seq 1-20 28K samples t = 0 . 2 t = 3 . 0 • Mini-batch size 1 Model ε x / ε y ε x / ε y • Epoch 1 Const. acc. 0.02 / 0.00 0.23 / 0.17 d = 5, f = linear 0.52 / 0.17 2.36 / 0.54 • Learning rate 10 − 6 d = 6, f = linear 0.23 / 0.01 1.23 / 0.07 • Momentum 0.9 d = 5, f = tanh - / - - / - • Loss function SE d = 6, f = tanh - / - - / - d = 5, f = cRelu 0.74 / 0.38 2.51 / 0.94 Test Results d = 6, f = cRelu 0.46 / 0.22 2.06 / 0.62 • Seq 21-25 7K samples • Position MAE R. Izquierdo | ITSC 2020, Virtual Conference

  39. Results 13 Training setup • Seq 1-20 28K samples t = 0 . 2 t = 3 . 0 • Mini-batch size 1 Model ε x / ε y ε x / ε y • Epoch 1 Const. acc. 0.02 / 0.00 0.23 / 0.17 d = 5, f = linear 0.52 / 0.17 2.36 / 0.54 • Learning rate 10 − 6 d = 6, f = linear 0.23 / 0.01 1.23 / 0.07 • Momentum 0.9 d = 5, f = tanh - / - - / - • Loss function SE d = 6, f = tanh - / - - / - d = 5, f = cRelu 0.74 / 0.38 2.51 / 0.94 Test Results d = 6, f = cRelu 0.46 / 0.22 2.06 / 0.62 • Seq 21-25 7K samples • Position MAE R. Izquierdo | ITSC 2020, Virtual Conference

  40. Results 13 Training setup • Seq 1-20 28K samples t = 0 . 2 t = 3 . 0 • Mini-batch size 1 Model ε x / ε y ε x / ε y • Epoch 1 Const. acc. 0.02 / 0.00 0.23 / 0.17 d = 5, f = linear 0.52 / 0.17 2.36 / 0.54 • Learning rate 10 − 6 d = 6, f = linear 0.23 / 0.01 1.23 / 0.07 • Momentum 0.9 d = 5, f = tanh - / - - / - • Loss function SE d = 6, f = tanh - / - - / - d = 5, f = cRelu 0.74 / 0.38 2.51 / 0.94 Test Results d = 6, f = cRelu 0.46 / 0.22 2.06 / 0.62 • Seq 21-25 7K samples • Position MAE R. Izquierdo | ITSC 2020, Virtual Conference

  41. Results 13 Training setup • Seq 1-20 28K samples t = 0 . 2 t = 3 . 0 • Mini-batch size 1 Model ε x / ε y ε x / ε y • Epoch 1 Const. acc. 0.02 / 0.00 0.23 / 0.17 d = 5, f = linear 0.52 / 0.17 2.36 / 0.54 • Learning rate 10 − 6 d = 6, f = linear 0.23 / 0.01 1.23 / 0.07 • Momentum 0.9 d = 5, f = tanh - / - - / - • Loss function SE d = 6, f = tanh - / - - / - d = 5, f = cRelu 0.74 / 0.38 2.51 / 0.94 Test Results d = 6, f = cRelu 0.46 / 0.22 2.06 / 0.62 • Seq 21-25 7K samples • Position MAE R. Izquierdo | ITSC 2020, Virtual Conference

  42. Results 13 Training setup • Seq 1-20 28K samples t = 0 . 2 t = 3 . 0 • Mini-batch size 1 Model ε x / ε y ε x / ε y • Epoch 1 Const. acc. 0.02 / 0.00 0.23 / 0.17 d = 5, f = linear 0.52 / 0.17 2.36 / 0.54 • Learning rate 10 − 6 d = 6, f = linear 0.23 / 0.01 1.23 / 0.07 • Momentum 0.9 d = 5, f = tanh - / - - / - • Loss function SE d = 6, f = tanh - / - - / - d = 5, f = cRelu 0.74 / 0.38 2.51 / 0.94 Test Results d = 6, f = cRelu 0.46 / 0.22 2.06 / 0.62 • Seq 21-25 7K samples • Position MAE R. Izquierdo | ITSC 2020, Virtual Conference

  43. Results 13 Training setup • Seq 1-20 28K samples t = 0 . 2 t = 3 . 0 • Mini-batch size 1 Model ε x / ε y ε x / ε y • Epoch 1 Const. acc. 0.02 / 0.00 0.23 / 0.17 d = 5, f = linear 0.52 / 0.17 2.36 / 0.54 • Learning rate 10 − 6 d = 6, f = linear 0.23 / 0.01 1.23 / 0.07 • Momentum 0.9 d = 5, f = tanh - / - - / - • Loss function SE d = 6, f = tanh - / - - / - d = 5, f = cRelu 0.74 / 0.38 2.51 / 0.94 Test Results d = 6, f = cRelu 0.46 / 0.22 2.06 / 0.62 • Seq 21-25 7K samples • Position MAE R. Izquierdo | ITSC 2020, Virtual Conference

  44. Results 13 Training setup • Seq 1-20 28K samples t = 0 . 2 t = 3 . 0 • Mini-batch size 1 Model ε x / ε y ε x / ε y • Epoch 1 Const. acc. 0.02 / 0.00 0.23 / 0.17 d = 5, f = linear 0.52 / 0.17 2.36 / 0.54 • Learning rate 10 − 6 d = 6, f = linear 0.23 / 0.01 1.23 / 0.07 • Momentum 0.9 d = 5, f = tanh - / - - / - • Loss function SE d = 6, f = tanh - / - - / - d = 5, f = cRelu 0.74 / 0.38 2.51 / 0.94 Test Results d = 6, f = cRelu 0.46 / 0.22 2.06 / 0.62 • Seq 21-25 7K samples • Position MAE R. Izquierdo | ITSC 2020, Virtual Conference

  45. Results 13 Training setup • Seq 1-20 28K samples t = 0 . 2 t = 3 . 0 • Mini-batch size 1 Model ε x / ε y ε x / ε y • Epoch 1 Const. acc. 0.02 / 0.00 0.23 / 0.17 d = 5, f = linear 0.52 / 0.17 2.36 / 0.54 • Learning rate 10 − 6 d = 6, f = linear 0.23 / 0.01 1.23 / 0.07 • Momentum 0.9 d = 5, f = tanh - / - - / - • Loss function SE d = 6, f = tanh - / - - / - d = 5, f = cRelu 0.74 / 0.38 2.51 / 0.94 Test Results d = 6, f = cRelu 0.46 / 0.22 2.06 / 0.62 • Seq 21-25 7K samples • Position MAE R. Izquierdo | ITSC 2020, Virtual Conference

  46. Results 14 Training setup • Seq 1-20 28K samples • Mini-batch size 1 • Epoch 1 • Learning rate 10 − 6 • Momentum 0.9 • Loss function SE Test Results • Seq 21-25 7K samples • Position MAE R. Izquierdo | ITSC 2020, Virtual Conference

  47. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  48. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  49. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  50. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  51. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  52. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  53. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  54. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  55. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  56. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  57. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  58. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  59. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  60. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  61. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  62. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  63. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  64. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  65. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  66. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  67. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  68. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  69. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  70. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  71. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  72. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  73. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

  74. Results 15 Results Example Input block example Output block example ( × = GT + = Pred ) R. Izquierdo | ITSC 2020, Virtual Conference

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