自動レーン変更と自動駐車のためのシミュレーション環境の構築

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1 自動レーン変更と自動駐車のためのシミュレーション環境の構築豊田工業大学スマートビークル研究センター三田誠一 2016 年 10 月 19 日 1

2 Titles of Contents Introduction Automated Parking Automated Lane Change Platooning Application 2

3 シミュレーション活用の利点まれにしか発生しない状況の再現ができるデータを再現性良く処理できる環境パラメータやセンサ種類を自由に設定できるシミュレーション環境から実環境への適応が簡単にできる開発アルゴリズムの初期段階での短時間性能検証ができる Application Examples Ontology Car Platooning Car Control Path Planning( Narrow Passage, Parking Model) Stereo Vision System Lane Change Modeling etc. 3

4 Application Examples Grand Truth Generated Depth Ontology Stereo Vision System Narrow Passage Path Planning Maze Path Planning Lane Change Path Planning 4

5 Narrow Passage and Automated Parking Path Planning Application 5

6 Level of Automated Parking General Parking Place Highly Structured Parking Place for Automated Driving Lower Speed Public Road Driving White Line Detection Pedestrian Detection Car Detection etc. Following Indications No Pedestrians, No Cars 6

7 Valet Parking 実環境 Predefined Routes Vehicle with 3D Dynamic Model 7

8 Simple Perception for Environment 視点変化 Driver View Top View 8

9 Subjects for Automated Parking 仮想環境構築 Narrow Passage Passing Obstacle Avoidance 9

10 General Parking Place 10

11 New appear obstacle Local path Intended parking position is occupied New empty space found New paths generated 11

12 Automated Parking Scheme Without pre-determined goal With pre-determined goal Parking area map Generate path from entrance to exit Follow planned path No Found parking position Yes Generate path to parking position Parking area map Update map Sensors data (laser, GPS, odometer ) Real time data Global path planner No Path No Yes Local path planner Path Yes Path follower Stop and Wait 12

13 Automated Parking Planner s Objectives The safety distance of the path from the obstacles The total travel distance of the path. The number of times that the vehicle has to switch the gear from forward to backward and vice versa. The curvature of the path to satisfy the kinematic constraints. N N 1 C( p ) w d( p ) w g( p ) g( p ) i 1 i 2 i i 1 i 1 i 1 N N 1 w p w ( p ) 3 i 4 i 1 i 1 i Closeness to Obstacles Gear Change g = 1: forward g = -1: backward Distance between Path Points Curvature 13

14 Application of Proposed Method for Automated Parking Proposed Parking Path Planning Method: 14

Fast Marching Method Introduced by Sethian in 1995* A numerical method for solving boundary value problems of the Eikonal equation: U F = 1 F > 0 :the front moving speed U : the travel time 1/F can

15 Fast Marching Method Introduced by Sethian in 1995* A numerical method for solving boundary value problems of the Eikonal equation: U F = 1 F > 0 :the front moving speed U : the travel time 1/F can also be known as objective cost Starting with an initial position for the front, the method systematically marches the front outwards one grid point at a time. * 15

16 Apply FMM on 2D Grid Map 1 st step: To find: - guiding path - distance from each cell to the goal Left side data Start 2D FMM path Right side data Goal 16

17 Apply SVM to Find Hyperplane and Safety Field 2 nd step: To find: distance to the hyperplane 17

Apply FMM to Continuous Search Space 3 rd step:

The neighbors of a node in search space Start 3D

18 Apply FMM to Continuous Search Space 3 rd step: continuous search space (x, y,, g) (x, y) : 2D coordination : vehicle heading angle (g {1,-1} ): driving maneuver (forward or backward) 2D neighbors The neighbors of a node in search space Start 3D continuous search space A path generated after applying FMM 18

Proposed Method Safety Margin for Obtained Paths (cell) 4.783 5.

19 Experimental Result for Complicated Path Case A Unstructured map with complicated obstacles shapes Hybrid A*and Risk Potential Proposed Method Method H-A*+RP Proposed Method Safety Margin for Obtained Paths (cell) Number of Gear Change 3 1 Average Curvature Average Computation Time (ms)

Proposed method Safety Margin for Obtained Paths (cell) 17.3708 19.

20 Experimental Result for Real Environment Case B Slam Map of real cluttered environment Hybrid A* and Risk Potential Proposed method Method H-A*+RP Proposed method Safety Margin for Obtained Paths (cell) Number of gear change 4 2 Average curvature Average computation time (ms)

21 Highly Structured Parking Place 21

22 Automated Parking Simulation 22

23 Another View for Parking 23

24 Conventional Camera View 24

25 Fisheye Camera View 25

26 Simulink Block from Parking Experiment

27 Ego Car Sensor Setting Fisheye cameras Ultrasonic sensors

28 Ego Car Simulink Blocks Predifined Path Car dynamic model Car State Trajectory follower Fisheye Cameras Input Data from Ultra Sonic Sensors Path planning and Control

29 Continue ---Ultrasonic sensors Output: Detected Obstacle Corresponding Range and Angle from Sensor Position

30 Ego Car Information Output : Ego Car State: Position in North West Direction, GPS position, Velocity, Yaw-angle

31 Trajectory Follower Input :Pre-defined Path, Car State, Desired Velocity Output: Steering angle, Throttle and Brake Control

32 Longitudinal Controller Trajectory Follower Block Lateral Controller

33 Car Controller Dynamic Model Input :Control Command Output :Simulated Ego Car State

34 Inside Car Dynamic Model

35 Path Planning and Control - Sensors Data Processing - Path planning - Car Control (Steering Wheel Angle, Velocity, Gear )

36 Distance Constraint Model for Automated Lane Change to Merge and Exit 36

37 Titles of Contents Why Automated Lane Change Related Work Lane Change Model Two Segments Lane Change Modeling Behavior Generation Model and Selection Motion Generation Model Simulation Results Comparison with Human Driver 37

Obstacles of Low Speed Moving Objects Fast Vehicle Distance

38 Why Automated Lane Change? ADAS/Semi Automated Driving/ Automated Driving Overtake Obstacles of Low Speed Moving Objects Fast Vehicle Distance Keeping Lane Departure Warning RADAR Sensor Merge or Exits to Highway Lane Change Assistant 38

39 Related Works State Transition Model Bayesian Network [D. Kasper et al., 2012] Hidden Markov Model [Y. Nishiwaki et al., 2010] Risk Assessment [D. Althoff, et al., 2012] Collision Estimation Based on Trajectories 39

40 Lane Change Model- Learn from Human Driver Human Lane Change Data Lane Change Experiment Two Segments Lane Change Model host vehicle Two Segments Model: 1- Segment 1 (Behavior Segment) Make Safe Space and Time Gap Deceleration to make free space/time for lane change Lane change and accelerate to adjust speed turning the steering wheel 2- Segment 2 (Motion Segment ) Smooth and Comfort Lane Change Segment 1 Segment 2 40

41 Situation Modelling & Estimation Lane change situation is modelled into a state occupancy grid with different size Most Critical Cell Occupied Estimation of Neighboring Vehicle Trajectory dback dfront dfront d min max{ v dback d' min max{ v ego back v v front ego,0} Time,0} Time Merge in and Exit Lane LC LC 41

Alternative Behaviors for Segment 1 (Behavior) Available Behaviors for Lane Change 1- Accelerate 2- Wait 3- Lane Change 1- Accelerate 2- Decelerate 3- Lane Change 4- Wait 1- accelerate 2- decelerate

42 Alternative Behaviors for Segment 1 (Behavior) Available Behaviors for Lane Change 1- Accelerate 2- Wait 3- Lane Change 1- Accelerate 2- Decelerate 3- Lane Change 4- Wait 1- accelerate 2- decelerate 3- lane change 4- wait 1- accelerate 2- lane change 3- wait 1- accelerate 2- wait 3- lane change 1- accelerate 2- wait 3- lane change 1- accelerate 2- decelerate 3- lane change 4- wait 1- decelerate 2- lane change 1- decelerate 2- lane change 3- wait 1- decelerate 2- lane change 3- wait 1-decelerate 2- wait 3- lane change 1-decelerate 2- wait 3- lane change 42

43 Alternative Behavior for Lane Change Behavior A : Lane change with current speed Velocity Lateral acc (m/s 2 ) T A T C Behavior C Behavior A Behavior B Time T B Time Behavior B: Lane change with deceleration Behavior C: Lane change with acceleration Behavior D: Wait Behavior A Behavior C Behavior B 43

44 Category Classification for Lane Change 1- accelerate 2- decelerate 3- wait 1- accelerate 2- wait 1- accelerate 2- decelerate 3- wait 1- accelerate 2- wait 1- accelerate 2- decelerate 3- lane change 4- wait Category A 1- accelerate 2- decelerate 3- lane change 4- wait Lane change 1- accelerate 2- lane change 3- wait 1- accelerate 2- decelerate 3- lane change 4- wait 1- decelerate 2- wait 1- decelerate 2- wait 1- accelerate 2- decelerate 3- wait 1- accelerate 2- wait 1- accelerate 2- wait 3- lane change 1- accelerate 2- wait 3- lane change 1- accelerate 2- wait 3- lane change 1- decelerate 2- lane change 1- decelerate 2- wait 1- accelerate 2- wait 1- decelerate 2- wait 1- decelerate 2- wait Category C 1- decelerate 2- lane change 3- wait 1- decelerate 2- lane change 3- wait 1-decelerate 2- wait 3- lane change 1-decelerate 2- wait 3- lane change Category B Category D Wait 44

45 Behavior Selection for Segment 1 Velocity Alternative 1: Accelerate and Lane Change Alternative 1: Accelerate and Lane Change dexit t min (meter) dexit t max (meter) Alternative 2: Decelerate and Lane Change Alternative 2: Decelerate and Lane Change T 1 T 2 Time For situation that has more than one behavior option Evaluation function for different action a = {acc, decc, wait} T J(a) = w jerk න ഺx 2 t + w safety (a) + w operationtime T(a) 0 Longitudinal Jerk Safety Time 45

46 Velocity Planning Acceleration Case : t 0 v(t 0 ) T Safety reserve v(t) r x(t 0 ) x lead (t 0 ) v lead (t 0 ) v lead (T) x(t) X Acceleration: x ሷ = f(x(t 0 ), v(t 0 ), x lead (t 0 ), v lead (t 0 ), T, r) Safety reserve: r = d min + TTR v lead (T) TTR: Time To React Error of the safety distance: Cost function: d t = x lead (t) r + t v lead t x(t) t 0 +T J = න (ω dist d t 2 + ω acc [ x(t)] ሷ 2 )dt Constraint: t 0 x T < L; x(t) ሷ < xሷ max Distance Constraint 46

47 Flowchart for Lateral Motion Generation-Segment 2 Lane Change is OK Making Alternative Trajectories v 1 v 2 Lane information Estimate Trajectory Position and speed of surrounding vehicle Cost Function Generate Alternative lateral trajectories Check collisions and find collision free Select Minimum cost function Send to path follower For execution Select Minimum Cost v 3 v 5 0 v h d t 1 time t 2 v 1 v 2 v 3 v 5 Minimum Cost Path t i C = න ഺy 2 t. dt + Δt i + κ t i k 2 d + න t i 1 lateral jerk heading error t i t i 1 κƴ 2 t dt smoothness 47

48 Automated Lane Change Flow Chart Image Sensor Laser Scanner / Radar Estimate Speed/Position of Neighboring Vehicles Estimate the Behavior of Neighboring Vehicles Lane Information Behavior A Segment 1: Do Lane Change Behavior B Segment 1: Wait Segment 2: Lane Change Generate Lateral/Longitudinal Trajectory for Neighboring Vehicles Make Grid Map Evaluation of Different Behaviors Set of Alternative Behaviors Behavior Selection Criteria's Generate Acceleration/ Deceleration/ Wait Patterns for Segment 1 Real Time Control & Execution Time buffer for re-evaluation (every timestamp: t milliseconds) Execution and Control Behavior C Segment 1: Accelerate Segment 2: Lane Change Behavior D Segment 1: Decelerate Segment 2: Lane Change Behavior A is Selected? yes no Generate Motion for Segment 2 Real time Environment Assessment 48

Behavior Generation 5- Automated Lateral/Longitudinal Motion Generation 6- Lane Detection Module 7- Automated Lane Change

49 Automated Lane Change Simulation Design and Implement Automated lane Change Simulation and Evaluation Environment Designed Modules 1- Adaptive Grid Generation 2- Estimate Surrounding Vehicles Trajectory 3- Check the Collision Avoidance 4- Automated Behavior Generation 5- Automated Lateral/Longitudinal Motion Generation 6- Lane Detection Module 7- Automated Lane Change Scenario Generation PreScan Simulation Platform 8- Control Steering and Acceleration 9-3D Graphic Simulator Simulink Model Flow diagram 49

50 Exiting with Acceleration Behavior Observation Grid Velocity Profile Clip 50

51 Exiting with Deceleration Behavior t=0, start Observation Grid Segment 1: decelerate Segment 2: lane change Exit Velocity Profile Clip 51

52 Merging in with Acceleration Behavior Merging in with Acceleration Behavior Observation Grid t=0, start Segment 1: accelerate Segment 2: lane change Velocity Profile Merge Clip 52

53 Merging with Deceleration Behavior t=0, start Observation Grid Segment 1: decelerate Segment 2: lane change Merge Velocity Profile Clip 53

54 Evaluation and Comparison with Expert Driver Lane Change Experiments Expert Driver Data Extraction - Lateral & Longitudinal Motion - x, ሶ x, ሷ y, ሶ yሷ - Lane information - Surrounding vehicle dx, dx, ሶ y Simulation(PreScan) Algorithms Behavior Model Simulation(PreScan) Comparison and Evaluation Motion Plan Feedback Human Computer 54

949) V=67[km/h] Surrounding Vehicle Driving Lane

55 Expert Driver - Data Extraction Velodyne Laser Map (18.798, ) V=67[km/h] Surrounding Vehicle Driving Lane Tracker y 3.5[m] x (-5.89, -6.86) V=96[km/h] Host vehicle V=54[km/h] 2.0[m] 1.5[m] (-21.3, ) V=70[km/h] 55

Collisions and Find Collision-free Select

56 Experiment Results Make Environment In PreScan Expert Driver Experiment Data Behavior and Motion Generation Generate Alternative Lateral Trajectories Check Collisions and Find Collision-free Select Minimum Cost Function Simulation of Real Traffic 56

57 Trajectory Evaluation 57

58 Platooning Application 58

59 Self-Defensive Maneuvering 59

60 Simulations Simulator PreScan Control strategy Longitudinal: PID Lateral: Steering angle 60

61 Result Rows: longitudinal, lateral, heading angle Columns: 1 st, 2 nd, 3 rd, 4 th platoon cars and the interfering car. 61

62 Simulation Result 62

63 Thank You for Your Attention! 63

2011 年東北地方太平洋沖地震の強震動生成のための震源モデル (2011 年 8 月 17 日修正版 )

2011 年東北地方太平洋沖地震の強震動生成のための震源モデル (2011 年 8 月 17 日修正版 ) Source Model for Generating Strong Ground Motions during the 11 March 2011 off Tohoku,Japan Earthquake (Revised on August 17, 2011) 入倉孝次郎倉橋奨愛知工業大学地域防災研究センター