Korea Autonomous Vehicle Contest 2013

Transcription

1 Korea Autonomous Vehicle Contest 2013 Smart Mobility Team (RTOS Lab. & Dyros Lab.) Graduate School of Convergence Science and Technology Seoul National University Page 1

2 Contents 1. Contest Information 1-1. Introduction of the Contest 1-2. Preparation for the Contest 2. Technical Information 2-1. Spirit Functional Technique Perception of Surroundings Localization Decision Path Planning & Tracking Page 2

3 1. Contest Information 1-1. Introduction of the Contest 1-2. Preparation for the Contest Page 3

4 1-1. Introduction of the Contest Page 4

5 1-1. Introduction of the Contest Summary Page 5 CONTEST NAME : KOREA AUTONOMOUS VEHICLE CONTEST 2013 DATE : 2013/10/11 LOCATION : Korea International Circuit HOST : MINISTRY OF TRADE. INDUSTRY & ENERGY SUPERVISION : KATECH(KOREA AUTONOMOTIVE TECHNOLOGY INSTITUTE) & KSAE(KOREA SOCIETY OF AUTOMOTIVE ENGINEERS) PARTNERS : Carnegie Mellon THE ROBOTICS INSTITUTE SPONSORS : HYUNDAI, KIA MOTORS, HYUNDAI MOBOIS, MANDO, MCNEX, Continental HOMEPAGE :

6 1-1. Introduction of the Contest Results Complete the full course 5 th in overall First participation in autonomous vehicle competitions Page 6

7 1-1. Introduction of the Contest Purpose Degree of Freedom of Sensor For Devel. of Original Tech DARPA Grand/Urban Challenge HYUDAI Autonomous Vehicle Contest For Devel. of Commercial Tech This Contest Main agent Led by Competitors Led by Company Led by Government Page 7

8 1-1. Introduction of the Contest Schedule Test # Date(2013) location Description 1st 2/4~2/5 KATECH LAB Test for Sensor Interface 2nd 2/18~2/19 KATECH PG Sensor Data Acquisition 3rd 3/29 KATECH PG Sensor Data Acquisition 4th 4/29~4/30 KATECH PG Sensor Data Acquisition & Motion Control Test 5th 5/30~5/31 KATECH PG Sensor Data Acquisition & Motion Control Test 6th 6/27~6/28 KATECH PG Sensor Data Acquisition & Motion Control Test 7th 7/18~7/19 KATECH PG Sensor Data Acquisition & Motion Control Test 8th 7/29~7/30 F1 Mission Test 9th 8/22~8/23 KATECH PG Sensor Data Acquisition & Motion Control Test 10th 8/26~8/27 F1 Mission Test 11th 9/26~9/27 KATECH PG Sensor Data Acquisition & Motion Control Test The Day 10/9~10/11 F1 Contest Page 8

9 1-1. Introduction of the Contest Missions(1) 1. Recognition of a traffic light direction 2. Recognition of a traffic light signal (i.e. sign, stop and go) Page 9

10 1-1. Introduction of the Contest Missions(2) 3. Falling obstacle avoidance 4. Recognition of a speed limit sign and control of the vehicle speed Page 10

11 1-1. Introduction of the Contest Missions(3) 5. Recognition of vehicles and path planning for avoidance 6. Recognition of a under-construction sign and path planning for avoidance Page 11

12 1-1. Introduction of the Contest Missions(4) 7. Recognition and avoidance of complex obstacles 8. Recognition of a pedestrian and stop in stop-zone Page 12

13 1-1. Introduction of the Contest Missions(5) 9. Recognition of the narrow road line and lane keeping 10. Recognition of a moving vehicle at the intersection Page 13

14 1-2. Preparation for the Contest Page 14

15 1-2. Preparation for the Contest Schedule Preparatory Period : Oct. 2012~ Oct Division Development Content Schedule Hardware Remodeling of the car and hardware configuration Purchasing the vehicle and sensor Constructing the hardware of the vehicle Maintenance Localization paper survey GPS & IMU sensor performance Analysis Development of the algorithms and test Integration test Perception Camera Lidar paper survey paper survey Camera Calibration & Software System LiDAR Calibration & software System Camera perception algorithms LiDAR perception algorithms Algorit hms Integr ation Field test & tuning Vehicle Control Lateral Control Vertical Control paper survey Vehicle Controller & Path Planning algorithm development test for integration the algorithms Field test & tuning Field test & tuning Integration System Integration paper survey Determining the protocol and development SW infrastructure algorithm test for mission test Page 15

16 1-2. Preparation for the Contest Testing Ground Gwanggyo Techno-Valley in Suwon Eumseong Ggot-dong-nae Eumseong New Town Korea Automotive Technology Institute PG Page 16

17 2. Technical Information 2-1. Spirit Functional Technique Page 17

18 2-1. Spirit-1 : Autonomous Vehicle by Smart Mobility Page 18

19 Hardware Information - Specification DGPS (B20) X 1 2DRMS 0.75m Frequency 10Hz Real-time Embedded System X 1 CompactRio Mono Camera (Dragonfly2) X 3 640x480, 30FPS Industrial PC X 2 I7 3.5GHz Hyundai HG240 Lidar (LMS-511) X 2 FOV 190 Deg Range 0~80m Laptop X 2 I7 2.3GHz Page 19 Smart Actuator (IG-52GM) X 1 MCU (ACC/BRK) X 2 LM3S8962

20 Hardware Information - Architecture USB CAN Ethernet (Lidar) Ethernet IEEE 1394 Serial DGPS Camera (Mono) PC2 PC3 Scanner Camera (Stereo) Scanner SWITCH USB to CAN PC1 CompactRIO (CRIO-9082 RT) Steering Wheel Control Accelerator Brake Gyro Scope and Vehicle Speed (ECU) Page 20

21 System Architecture (1/2) Composition & Function of Subsystem Autonomous System Vision System Laser System Decision System Localization System Path Planning System Path Tracking System Vehicle Control System Descriptions Data acquisition & processing for start, progress and finish each mission using vision sensor. The system can perceive traffic lights, traffic signs, stop line, etc. Obstacles data acquisition & map construction using LiDAR. The system can perceive Fallen objects, barricades, complex obstacles, vehicles, pedestrians Situation awareness & velocity decision using data from the vision, laser & localization system. Data acquisition & processing for vehicle position, velocity and heading angle Path planning according to information about missions or obstacles based on driving map. Steering angle decision according to look-ahead position & length Brake & acceleration value decision according to desired velocity. Steering Angle Control Brake and Acceleration Control Page 21

22 System Architecture (2/2) Flow Chart of System Vision System Laser System Mission & Object Information Mission Tag Decision System Mission & Object Information Static Map Drivablility Map Mission Target Velocity Path Planning System Vehicle Position Vehicle Heading Angle Localization System Look Ahead Position & Length Target Velocity Vehicle Control System Steering Wheel Angle Brake & Accel Position Path Tracking System Page 22

23 Communications (1/2) 3 Severs depending on each purpose. Server for image processing placed on decision system It processes data to recognize each mission. Server for grid maps placed on decision system It processes data to compose driving map. Server for localizations placed on localization system It processes data to obtain localization information(vehicle position, velocity and heading angle) It publish and subscribe this data periodically. TCP/IP Computer Vision System Laser and Decision System <Relation with Server & Client> C C S C C C: Send a message to server S: Broadcasting messages Image processing port Grid map port Image processing connection Grid map connection UDP/IP Localization port Path Tracking System Page 23 Path Planning System Localization System Localization connection CAN

24 Communications (2/2) Block Diagram of Subsystems for Communications Page 24

25 2-1. Functional Technique Perception Localization Path Planning Vehicle Control Page 25

26 Perception of Surroundings Page 26

27 Vision System Traffic Signal Perception 1) Traffic Light Perception Single image Multi images Detection Classification Tracking & Decision Left Left? Right? Left, Left, Right, Left Left Color based detection (RGB&HSV[1] threshold Blob labeling) Learning based detection (PCA[2] feature extraction SVM[3] classifier) Point tracking & Result voting (Deterministic tracking[4] Result voting) RGB HSV PCA SVM Page 27

28 Vision System Traffic Signal Perception 2) Traffic Sign Perception Single image Multi images Detection Classification Tracking & Decision Learning based detection (Haar-like feature extraction Cascade classifier[6]) Learning based detection (PCA feature extraction SVM classifier) Ring buffer & Result voting (Simple ring buffer Result voting) Haar-like PCA SVM Cascade Page 28

29 Vision System Traffic Signal Perception 3) Library for Developing Perception System We developed traffic signal perception system using OpenCV.( that serves qualified source code. OpenCV was helpful to us for following image process topics. - Image Processing - Machine Learning - Object Detection 4) Camera for Developing Perception System Dragonfly2 is used as image sensor made by Point Grey. ( SDK of Point Grey Camera supplies various functions for developing system. Page 29

30 Vision System Detection of Lane Markers 1) System Overview Top view selective Gaussian spatial filters thresholding Hough transform RANSAC line fitting (1) Top View Perspective View Remove perspective effects, using the inverse perspective mapping Focus on only a subregion of the input image, which helps in reducing the run time Reslult data can be transformed directly in real world coordinate (2) selective Gaussian spatial filters Edge Detection Simple and robust than the edge detection Reduce computing time, using separable kernel Optimized to detecting vertical, horizontal lines Using separable kernel Top View + Filtering Perspective View + Edge Detection Page 30 30

31 Vision System Detection of Lane Markers 2) Result Lane, stop lane detection Speed bump detection Page 31 31

32 Lidar System A scan point clustering algorithm Laser Data Acquisition Line-fitting & Coner fitting Segmentation & Feature Extraction Object Queue Mission Detection Grid Map Generation Local Grid Map Mission & Object Information (Laser) Decision System Page 32

33 Perception of Surroundings Reference [1] HSV color space : [2] PCA (with regard to face recognition) : [3] SVM : ht=svm [4] Deterministic tracking : A. Yilmaz, O. Javed, and M. Shah, Object tracking: A survey, ACM Comput. Surv., vol. 38, no. 4, pp. 1 45, [5] Cascade Classification & Haar-like feature : jones [6] Lane Detection : [7] Camera Laser Calibration : Page 33

34 Localization? Page 34

35 Localization - Overview Flow: Input Processing Prediction Update Output Computation Compensation the Predict heading angle using Update GPS heading Transformation Path Yaw rate offset integration the Yaw rate (100Hz) angle (10Hz) planning/tracking Analyze NMEA Predict the position using Update GPS position coordinate protocol integration Velocity (50Hz) (10Hz) Revise OBD II velocity Sensor Data Processing Gyroscope Data Processing Yaw Rate Heading Angle Prediction Kalman Filter I Heading Angle Heading Angle Update Heading Angle Output Data Computation Heading Angle Conversion DGPS Data Processing Heading Angle Latitude, Longitude Coordinate Conversion (GRS-TM) (X, Y) Kalman Filter II Wheel Speed Data Processing Velocity (X, Y) (X, Y) Position Prediction Position Update Position Conversion Page 35 Velocity Computation

36 Localization GPS Characteristic DGPS B20 1.5m RMS horizontal error 10 Hz output rate Heading Angle (degree) Time (s) True GPS < Heading Angle error in low velocity section> < GPS error with path curvature > Page 36

37 Localization GPS Complementation Dynamic Adjustment of Noise Covariance Apply a vehicle velocity and yaw rate to noise covariance K k = P k H T (HH k H T + R) 1 K k : Kalman gain P k : error covariance at time k H: observation model between the state and the observation R: measurement noise covariance matrix R = R v sssssss(v V ttttttttt ) + Rr sssssss( γ + γ ttttttttt ) Average heading error (degree) Velocity from wheel speed sensor (m/s) = R v 1+ e V V ttttttttt + R r 1+ e γ + γ ttttttttt R v, R r : coefficient V : velocity of vehicle γ : yaw rate of vehicle V ttttttttt : threshold of velocity γ ttttttttt : threshold of yaw rate Average heading error (degree) Yaw rate (degree/s) Page 37

38 Decision Page 38

39 Decision for each mission Page 39

40 Decision for each mission Page 40

41 Decision for each mission Page 41

42 Decision for each mission Page 42

43 Decision Decision Flow Localization System Vision System Lidar System New Tag (Vision) New Tag (Lidar) Priority based Tag Manager Current Mission Info Time-Out & Distance-Out Observer Static Map Drivability Map Generator Mission Velocity Manager Drivability Map Mission Velocity Page 43

44 Path Planning & Tracking - System Overview Decision System Mission Velocity Drivability Map Localization Information Predefined Velocity Threaten Assessment Path Planning Velocity Decision Look Ahead Decision Target Velocity Look Ahead Point & Length Path Tracking Algorithms Brake & Accel Position Steering Wheel Angle Page 44 Vehicle Control System

45 Path Planning - Overview Potential Field based A-star Algorithm Object Mapping Object Around Field Mapping Search (A star Algorithm) Smooth Path (Bezier curve) Page 45

46 Path Planning - Algorithm Potential Field based A-star Algorithm In curve road, it does not generate proper obstacle s potential field Rotate potential field based on road curvature Page 46

47 Path Tracking - Candidates of Algorithm Pure pursuit Stanley Vector Pure pursuit It tracks look ahead point. It doesn t take into account look ahead point s orientation. Inflection region like intersection region is vulnerable because of selecting the nearest point with front wheel point It considers orientation tracking. Vehicle s orientation affects significantly steering angle. Page 47

48 Path Tracking - Pure-Pursuit Algorithm L3 L2 L1 X Y Pure-Pursuit algorithm Change target steering angle based on Look-ahead length Page 48

49 Page 49 Thank you!