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Steering Behaviors for Autonomous Vehicles in Virtual Evironments

Steering Behaviors for Autonomous Vehicles in Virtual Evironments. Hongling Wang Joseph K. Kearney James Cremer Department Of Computer Science University of Iowa Peter Willemsen School of Computing University of Utah. Focus. Control of Autonomous Vehicles in VE Ambient traffic

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Steering Behaviors for Autonomous Vehicles in Virtual Evironments

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  1. Steering Behaviors for Autonomous Vehicles in Virtual Evironments Hongling Wang Joseph K. Kearney James Cremer Department Of Computer Science University of Iowa Peter Willemsen School of Computing University of Utah

  2. Focus • Control of Autonomous Vehicles in VE • Ambient traffic • Principal roles in scenarios • Importance of Road Representation • Frame of reference • Natural coordinate system • Intersection and Lane Changing Behaviors • Complex interactions among vehicles • Limits of independent control

  3. Motivation • VE as Laboratories for Studying Human Behavior • Developmental differences in road crossing • The influence of disease, drugs, and disabilities • Design of in-vehicle technology • Cell phones, navigation aids, collision warning

  4. Bicycle Simulator Video

  5. Gap Acceptance in the Hank Bicycle Simulator

  6. Related Work • Flocking • Complex group behavior from simple rule-based behaviors (Reynolds) • Hierarchical Distributed Contol • Independent, goal-oriented sub-behaviors (Badler et al.; Blumberg and Galyean; Cremer, Kearney, and Papelis) • Driving • Simulation (Donikian; Lemessi) • ALV (Coulter, Sukthankar; Wit, Crane, and Armstrong) • Human Driving Behavior (Ahmed; Boer, Kuge, and Yamamura; Fang, Pham, and Kobayashi; Salvucci and Liu)

  7. Roadway Modeling • Roads as Ribbons • Oriented Surface • Smooth Strips • Twist and turn in space • Central Axis • Arc-length parameterized curve • Twist Angle • Linked through Intersections

  8. Ribbon • Ribbon coordinate system • Distance, Offset, and loft (D,O,L) • Egocentric frame of reference • Efficient Mapping (D,O,L) (X,Y,Z)

  9. Intersections—Where Roads Join • Shared regions • Non-oriented • Corridors connect incoming and outgoing lanes • Single lane ribbons • Annotated with right-of-way rules

  10. Ribbon to Ribbon Transitions • Problem: Tangle of Ribbons Bookkeeping Tedious and Error Prone • Possible switch in orientation • Possible shift in alignment • Solution:Paths • Composite ribbons

  11. Path • One-lane Overlay • Removes transitions between ribbons • Immediate Plan of Action • Highly dynamic • Natural frame of reference

  12. Distributed Control • Multiple, Independent Controllers • Each responsible for some aspect of behavior • e.g. Cruising, Following • Compete for control • Control Parameters • Acceleration • Steering Angle

  13. Road Tracking • Non-holonomic constraint Rolling wheels Move on a circle • Pursuit point control • Steer to a point on the path • Look-ahead distance

  14. Controlling Speed • Cruising: Proportion Control • Following: Proportional Derivative Controller

  15. Intersection Behavior • Gates access to shared regions • Decision: Go / No Go • Action: stop at stopline

  16. Gap Acceptance • Based on Interval Analysis • Right-of-way rules encoded in DB • Corridors as resources Compare crossing intervals c0 c2 c1 time tenter texit

  17. Intersection Exceptions • Problem: deadlock Double blocked threats • Solution: Recognition and response • Problem: starvation Unending stream of opposition • Solution: Guaranteed progress

  18. What’s missing? • Where do paths come from? • Vehicles meander Pick corridors Add outgoing road • No goal seeking behavior • Need directions “Turn right at the first intersection, drive through two intersections, and then turn left.”

  19. Route • A succession of roads and intersections • Like MapQuest Directions • A global, strategic goal • The path must conform to the route • May require lane changes

  20. Stages of Lane Changing • Motivation Why change lanes? • Decision Choosing a target lane Deciding when to go • Action How to change lanes?

  21. Motivation to Change Lanes • Discretionary Lane Change (DLC) to improve driving conditions (e.g. speed, density) • Mandatory Lane Change (MLC) to meet destination requirements (e.g. lane termination) Decision to Initiate a Lane Change • Best conditions (e.g. flow) • Gap Acceptance • Lead gap • Lag gap

  22. Lane Changing Action • Shift Pursuit Point • Proportional Derivative Controller • Speed Coupling

  23. Behavior Combination • Combine accelerations from • Cruising behavior • Following behavior • Intersection behavior • Combine steering angle from • Tracking behavior • Lane changing behavior

  24. Interactions Between Controllers • Problem: impeded progress Following prevents overtaking • Solution: Reduce following distance Stiffen controller • Problem: unveiled threat Appearance of leader in new lane • Solution: Split attention – follow 2 leaders

  25. Summary • An accurate, efficient, robust roadway model • Ribbon network • Arc length parameterization • Efficient mapping between ribbon and Cartesian coordinates • A framework for modeling behaviors • Ribbon based tracking • Path based behaviors • Route as a strategic goal

  26. Future Work • Pedestrians • Modeling non-oriented navigable surfaces (e.g. intersections) • Pursuit Point Control • Behavioral Diversity

  27. Acknowledgments • NSF Support: INT-9724746, EIA-0130864, and IIS-0002535 • Contributing students, staff, faculty Jodie Plumert Geb Thomas David Schwebel Pete Willemsen Penney Nichols-Whitehead HongLing Wang Jennifer Lee Steffan Munteanu Sarah Rains Joan Severson Sara Koschmeder Tom Drewes Ben Fraga Forrest Meggers Kim Schroeder Paul Debbins Stephanie Dawes Bohong Zhang Lloyd Frei Zhi-hong Wang Keith Miller Xiao-Qian Jiang

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