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DYROS Autonomous Valet Parking 2019

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DYROS Autonomous Valet Parking 2019

  1. 1. Autonomous Valet Parking Dynamic Robotic System Lab. (DYROS) Autonomous Vehicle Team Goal & Assumption & System Architecture 0. Global Path Planning 1. Drivable Area Perception 2. Intersection Decision 3. Driving Policy 4. Parking Position and Direction Detection 5. Parking Path Planning and Tracking 6. Results
  2. 2. • Goal: Fast stable autonomous valet parking • Assumption: Topological global map is given. Parking lot environment (v-rep simulator) Autonomous Valet Parking Goal & Assumption GyuBeom Im, Minsung Kim, Joonwoo Ahn, Minsoo Kim, Jaeheung Park / S i m u l a t o r f o r A u t o n o m o u s V e h i c l e R e s e a r c h i n P a r k i n g l o t E n v i r o n m e n t T h e K o r e a n S o c i e t y o f A u t o m o t i v e E n g i n e e r s A n n u a l A u t u m n C o n f e r e n c e , 2 0 . 1 1 . 2 0 1 8 . : Vertex (Intersection) : Edge (Road)
  3. 3. Autonomous Valet Parking System Architecture Perception (Map) Autonomous Vehicle 0. 전역 경로 계획 AVM (AroundViewMonitor) Topological 전역 지도 Front Camera 3. 주행 정책 Driving Policy (Control)Vehicle Controller Navigation Info. 4. 주차 가능 영역 인식 8. 주차 경로 추종 Vertex & Edge 주차 가능 위치 Path Planning 7. 주차 경로 생성 경로 2. 교차로 판단 직진/비-직진 도로 Bounding Box 6. 주행 가능 영역 인식 주행 가능/불가능 지도 If, Parking space is detected. Parking Complete Start Valet Parking Velodyne 5. SLAM Localization Data Autonomous Valet Parking System Before Detecting Parking Goal After Detecting Parking Goal
  4. 4. 0. Global Path Planning Steering Angle, Accel./Brake Steering Velocity AVM (AroundViewMonitor) Topological Global Map Front Camera 1. Drivable Area Perception 3. Driving Policy Vehicle Controller Drivable/Non-Drivable Map Navigation Info. Look-ahead Point 4. Parking Goal Detection 8. Parking Path Tracking Look-ahead Point Vertex & Edge Parking Pose 7. Parking Path Planning Path 2. Intersection Decision Straight/Non-Straight Road Bounding Box 6. Drivable Area Perception Drivable/Non-Drivable Map Velodyne 5. SLAM Localization Data Velocity Autonomous Valet Parking System Architecture Perception (Map) Autonomous Vehicle Driving Policy (Control) Path Planning If, Parking space is detected. Parking Complete Start Valet Parking Autonomous Valet Parking System
  5. 5. 𝛾𝑖: The weight for the order of visiting (= 𝑁−𝑖 𝑁 ) 𝑝𝑖: The parking availability on the 𝑖 𝑡ℎ road (𝐸𝑖 ∈ {𝐸 𝑅, 𝐸 𝑂}), 𝐸𝑖 : The length of the 𝑖 𝑡ℎ road 𝑐𝑖: The forward-backward switching of a vehicle on the 𝑖 𝑡ℎ road Autonomous Valet Parking 0. Global Path Planning Rural Postman Problem (RPP) Proposed Method Original Method • Goal: Visiting roads with parking spaces(𝐸𝑅) preferentially, while driving all parking space Minsoo Kim, Joonwoo Ahn, Jaeheung Park / G l o b a l P l a n n i n g f o r V i s i t i n g R o a d s w i t h P a r k i n g S p a c e s i n P r i o r i t y Using Rural Postman Problem I T S C 2 0 1 9 T h e 2 2 n d I E E E I n t e r n a t i o n a l C o n f e r e n c e o n I n t e l l i g e n t T r a n s p o r t a t i o n S y s t e m s , Auckland, NZ, October 27-30, 2019 • Method: : Global planning for visiting all required edge (𝐸 𝑅) and some optional edges (𝐸 𝑂). ` R L S S R L : parking space O (𝑬 𝑹) : parking space X (𝑬 𝑶)
  6. 6. 0. Global Path Planning Steering Angle, Accel./Brake Steering Velocity AVM (AroundViewMonitor) Topological Global Map Front Camera 1. Drivable Area Perception 3. Driving Policy Vehicle Controller Drivable/Non-Drivable Map Navigation Info. Look-ahead Point 4. Parking Goal Detection 8. Parking Path Tracking Look-ahead Point Vertex & Edge Parking Pose 7. Parking Path Planning Path 2. Intersection Decision Straight/Non-Straight Road Bounding Box 6. Drivable Area Perception Drivable/Non-Drivable Map Velodyne 5. SLAM Localization Data Velocity Autonomous Valet Parking System Architecture Perception (Map) Autonomous Vehicle Driving Policy (Control) Path Planning If, Parking space is detected. Parking Complete Start Valet Parking Autonomous Valet Parking System
  7. 7. • Goal: To use VGG Net. at unstructured environment (parking lot) • Method: Making 300 data and training VGG Net. (using pre-trained weight) (Train data: 200, Validation data: 100) *Untrained Environment Avg. Precision Avg. Frames/sec 95.4 % 9.7 Autonomous Valet Parking 1. Drivable Area Perception x 2 Chanwoo Ahn, Jaeheung Park / R e a l - T i m e S e g m e n t a t i o n o f D r i v a b l e A r e a U s i n g S u p e r v i s e d L e a r n i n g Conference, Ubiquitous Robots 2019 (The 16th International Conference on Ubiquitous Robots), Jeju, Korea, 24.06.2019.
  8. 8. • Goal: To determine if the road is an intersection. • Method: Using YOLO-v3, training Straight/Non-Straight road bounding box (using pre-trained (data: 500)) ➔ If there are two bounding boxes, it is determined that the road is an intersection. Autonomous Valet Parking 2. Intersection Decision * Trained Environment
  9. 9. Autonomous Valet Parking 3. Driving Policy – Global path & localization data O • Goal: Tracking global path and avoid an obstacle • Method: Generate Hybrid A* path around an obstacle. Joonwoo Ahn, Minsoo Kim, GyuBeom Im, Minsung Kim, Jaeheung Park / D e v e l o p m e n t o f A u t o n o m o u s V a l e t P a r k i n g S y s t e m a n d V e h i c l e T e s t T h e K o r e a n S o c i e t y o f A u t o m o t i v e E n g i n e e r s A n n u a l A u t u m n C o n f e r e n c e , 2 0 . 1 1 . 2 0 1 8 . *Path Tracking: Pure Pursuit
  10. 10. • Limitation: Localization data error → Planned Global Path ≠ Tracking Global Path → Collision Possibility ↑ Fail to match with the global map 3-D Global SLAM (Cartographer) Planned Global Path Tracking Global Path Autonomous Valet Parking 3. Driving Policy – Global path & localization data O Limitation Collision • Method: Suggest method to not use the localization data when driving unstructured environments (parking lot)
  11. 11. Navi. Info. Navi. Info. • Goal: In an unstructured environments, drives do not use use the global path and localization data (with considering Navi. Info.). • Method: Selects the look-ahead point with 3 criteria (1. drivable area, 2. can avoid obstacles, 3. far from the ego vehicle) at CARLA simulator, and trains Neural Net.(MobileNetV2) [Imitation Learning] - At this time, only the bounding box according to Navi. Info. are considered to be drivable area. Autonomous Valet Parking 3. Driving Policy – Global path & localization data X
  12. 12. Navi. Info. Autonomous Valet Parking 3. Driving Policy(+Navi. Info.) – Imitation Learning Result
  13. 13. 0. Global Path Planning Steering Angle, Accel./Brake Steering Velocity AVM (AroundViewMonitor) Topological Global Map Front Camera 1. Drivable Area Perception 3. Driving Policy Vehicle Controller Drivable/Non-Drivable Map Navigation Info. Look-ahead Point 4. Parking Goal Detection 8. Parking Path Tracking Look-ahead Point Vertex & Edge Parking Pose 7. Parking Path Planning Path 2. Intersection Deceision Straight/Non-Straight Road Bounding Box 6. Drivable Area Perception Drivable/Non-Drivable Map Velodyne 5. SLAM Localization Data Velocity Autonomous Valet Parking System Architecture Perception (Map) Autonomous Vehicle Driving Policy (Control) Path Planning If, Parking space is detected. Parking Complete Start Valet Parking Autonomous Valet Parking System
  14. 14. Autonomous Valet Parking 4. Parking Position and Direction Detection • Method: YOLO v3 (Position) + Hough Line Detection (heading) If, parking space is detected. → Slow down If, vehicle’s center == position of parking goal → Stop
  15. 15. Autonomous Valet Parking 5. Parking Path Planning Dubins Path Hybrid A* Desired Orientation-RRT* Time 0.00001 sec about 0.2 sec Limit 2.0 sec Deviation None Low High Limitation Not available if there are obstacles around the path. It may not be possible to generate a path, depending on the parameters. Complicated paths with frequent forward and reverse transitions are generated (20 %).
  16. 16. Autonomous Valet Parking 6-1. Simulation Test Result
  17. 17. Autonomous Valet Parking 6-2. Real Vehicle Test Result 1.
  18. 18. Autonomous Valet Parking 6-3. Real Vehicle Test Result 2.
  19. 19. Thank you

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