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Classification of Human's Driving Behavior using Support Vector Machine

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This is a presentation slides for Project Work as part of RWDA 2015.

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Classification of Human's Driving Behavior using Support Vector Machine

  1. 1. Classification of Human’s Driving Behavior Using Support Vector Machine Graduate School of Information Science Edahiro & Kato Laboratory Yuki Kitsukawa yuki@ertl.jp 1 RWDA 2015: Project Work
  2. 2. BACKGROUND
  3. 3. Background Realization of Human-Friendly Autonomous Driving Machine Learning approach: Learn how human drives a car according to surrounding condition.
  4. 4. Objective Hypothesis It is possible to judge how to control vehicles based on learning models. Verification Method – Support Vector Machine 1. Create learning model of surrounding environment and driving behavior 2. Classify … • Whether or not the driver steps the brake based on surrounding environment • If there are pedestrian around the vehicle based on driving behavior
  5. 5. DATASET
  6. 6. Dataset Grasshopper3 (Camera) Velodyne HDL-64E (LIDAR) Experimental vehicle CardBUS (CAN)
  7. 7. The dataset
  8. 8. Dataset 1-second intervals. 160 data CAN signal camera velodyne velocity steering angle gas pedal brake pedal # of pedestrian dist. to Pedestrian pedestrian brake 1 0 4.5 0 3408 0 0 0 1 2 0 4.5 31 3715 1 29.654 1 1 3 0 4.5 36 3320 0 0 0 1 4 0 4.5 17 3759 0 0 0 1 5 0 4.5 0 2961 0 0 0 1 6 0.46 7.5 37 177 0 0 0 0 7 2.07 9 0 309 0 0 0 0 8 3.27 9 25 704 0 0 0 1 9 4.06 9 44 934 0 0 0 1 10 4.17 9 45 1075 1 14.1047 1 1 11 4.19 3 0 1049 1 33.491 1 1 12 4.06 -43.5 0 412 0 0 0 1 13 4.93 -196.5 0 252 0 0 0 0 14 5.28 -321 50 269 0 0 0 0 15 5.12 -433.5 30 635 0 0 0 1
  9. 9. ANALYSIS METHOD
  10. 10. Analysis Method Pattern 1 surrounding environment → driving behavior Input: velocity, steering angle, # of pedestrian, distance to pedestrian Output: 0:not pedal brake, 1: pedal brake
  11. 11. Analysis Method CAN signal camera velodyne velocity steering angle gas pedal brake pedal # of pedestrian dist. to Pedestrian pedestrian brake 1 0 4.5 0 3408 0 0 0 1 2 0 4.5 31 3715 1 29.654 1 1 3 0 4.5 36 3320 0 0 0 1 4 0 4.5 17 3759 0 0 0 1 5 0 4.5 0 2961 0 0 0 1 6 0.46 7.5 37 177 0 0 0 0 7 2.07 9 0 309 0 0 0 0 8 3.27 9 25 704 0 0 0 1 9 4.06 9 44 934 0 0 0 1 10 4.17 9 45 1075 1 14.1047 1 1 11 4.19 3 0 1049 1 33.491 1 1 12 4.06 -43.5 0 412 0 0 0 1 Input Output
  12. 12. Analysis Method Pattern 2 driving behavior → surrounding environment Input: velocity, steering angle, gas pedal, brake pedal Output: 0:no pedestrian, 1: pedestrian
  13. 13. Analysis Method CAN signal camera velodyne velocity steering angle gas pedal brake pedal # of pedestrian dist. to Pedestrian pedestrian brake 1 0 4.5 0 3408 0 0 0 1 2 0 4.5 31 3715 1 29.654 1 1 3 0 4.5 36 3320 0 0 0 1 4 0 4.5 17 3759 0 0 0 1 5 0 4.5 0 2961 0 0 0 1 6 0.46 7.5 37 177 0 0 0 0 7 2.07 9 0 309 0 0 0 0 8 3.27 9 25 704 0 0 0 1 9 4.06 9 44 934 0 0 0 1 10 4.17 9 45 1075 1 14.1047 1 1 11 4.19 3 0 1049 1 33.491 1 1 12 4.06 -43.5 0 412 0 0 0 1 Input Output
  14. 14. ANALYSIS RESULT
  15. 15. Pattern 1 Positive: step brake pedal, Negative: not step brake pedal 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Linear Quadratic Polynomial RBF MLP Rate Kernel Function Pattern 1 False Negative False Positive True Negative True Positive 77.2% 79.6% 88.3% 83.3% 62.3%
  16. 16. Pattern 2 Positive: pedestrian, Negative: no pedestrians 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Linear Quadratic Polynomial RBF MLP Rate Kernel Function Pattern 2 False Negative False Positive True Negative True Positive 56.8% 83.3% 88.3% 86.4% 57.4%
  17. 17. CONCLUSION
  18. 18. Conclusion I researched the relationship between surrounding environment and driving behavior through classification using Support Vector Machine Surrounding Environment → Driving Behavior Whether to step break pedal: 88.3% Driving Behavior → Surrounding Environment Whether there is a pedestrian: 88.3%
  19. 19. Future Work • Feature value – Relative Position of pedestrian, vehicle – Driving area (traffic environment, city, rural area…) – Pedestrian’s direction – Traffic Light – Vehicle’s destination – … • Collect more dataset • Parameter Tuning

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