Classification of Human's Driving Behavior using Support Vector Machine
•Download as PPTX, PDF•
0 likes•500 views
This is a presentation slides for Project Work as part of RWDA 2015.
Recommended
Recommended
More Related Content
Similar to Classification of Human's Driving Behavior using Support Vector Machine
Similar to Classification of Human's Driving Behavior using Support Vector Machine (20)
Recently uploaded
Recently uploaded (20)
Classification of Human's Driving Behavior using Support Vector Machine
- 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. BACKGROUND
- 5. Background Realization of Human-Friendly Autonomous Driving Machine Learning approach: Learn how human drives a car according to surrounding condition.
- 6. 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
- 7. DATASET
- 8. Dataset Grasshopper3 (Camera) Velodyne HDL-64E (LIDAR) Experimental vehicle CardBUS (CAN)
- 9. The dataset
- 10. 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
- 11. ANALYSIS METHOD
- 12. 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
- 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. Analysis Method Pattern 2 driving behavior → surrounding environment Input: velocity, steering angle, gas pedal, brake pedal Output: 0:no pedestrian, 1: pedestrian
- 15. 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
- 16. ANALYSIS RESULT
- 17. 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%
- 18. 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%
- 19. CONCLUSION
- 20. 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%
- 21. 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
Editor's Notes
- Today, I want to talk about how the surrounding environment around the vehicle affects the driver’s behavior.
- 国内外 自動車業界 センサー 自動運転 研究開発 活発 自動運転 高齢者の移動支援 無人タクシー 物流を変える 我々も開発している 違い オープンソース プラットフォーム 幅広い機能 多くのセンサー デバイス UI 自分 位置推定
- The objective of this project is to invest whether there is a relation between surrounding environment and driver’s behavior especially I focused on the driver steps the brake pedal.
- The objective of this project is to invest whether there is a relation between surrounding environment and driver’s behavior especially I focused on the driver steps the brake pedal.
- Here, I will explain about the dataset. How I acquire the data. These are the sensors I used in this experiment. Grasshopper3 is the camera installed to capture the image of front of vehicle. This time the camera is used to detect pedestrians in front of the vehicle. Velodyne HDL-64E is the laser scanner installed on top of the vehicle to recognize the objects around the vehicle. This time velodyne is used to measure the distance from the vehicle to the pedestrian. CAN(Controller Area Network) signal is acquired through the CardBUS connected to the vehicle. From CAN signal, we can find the driver’s behavior. For example, the velocity of the car, how the driver step the accel, brake, how degree the driver turn the steering and so on. In this project, I combined the data acquired through these sensors.
- To acquire the data, I conducted field operation experiment in imitation city in Toyota. Combining the image and velodyne data, we can estimate the distance to the pedestrian.
- This is the dataset I acquired through experiment. Ispedestrian is the flag. If there are pedestrian captured by camera, it will be 1. Brakepress is the flag, if the driver steps the pedal, it will be 1.
- This is the dataset I acquired through experiment. Ispedestrian is the flag. If there are pedestrian captured by camera, it will be 1. Brakepress is the flag, if the driver steps the pedal, it will be 1.
- This is the dataset I acquired through experiment. Ispedestrian is the flag. If there are pedestrian captured by camera, it will be 1. Brakepress is the flag, if the driver steps the pedal, it will be 1.
- Here, I want to conclude my project. I built the SVM classifier. The accuracy rate of pattern 1 is 77% and the pattern 2 is 56.7%. I can say that accuracy rate of pattern 1 is relatively high. In other words, It can be predictable whether the driver step the brake pedal according to the surrounding environment. However, it is difficult to estimate whether there is pedestrian from the CAN signal. To improve the accuracy rate, it is necessary to improve the detection of pedestrian. The pedestrian detection program used in this experiment often makes miss-detection. There is a room to improve the detection. The second is, here I took into consideration whether there is a pedestrian or not, so it is good way to think pedestruan’s direction, for example, the pedestrian is walk along the road or the pedestrians is about to cross the road. Third is capturing other vehicles round itself. The other is adding sensors, backward camera or laser scanner and so on. And considering other algorithm for machine learning.