1. Efficient Roadway Modeling and Behavior Control for Real-time Simulation Hongling Wang Department Of Computer Science University of Iowa Oct. 28, 2004

2. Overview Research introduction Motivation Model of roadways Behavior control on roadways Contributions Future work

3. Research Introduction Dynamic Virtual Environment –Vehicles, pedestrians, etc… –Lots of them! Roadway Modeling –Put some activities on roadways Behaviors –Control the activities happing on roadways

4. Motivation Virtual environments –Laboratories for psychology –Understanding driver/rider behavior –Test future car concepts More applications

5. Roadway Modeling Ribbon network –Modeling roads, streets, sidewalks, and other navigable ways as ribbons Ribbon defines geometry and orientation of navigable surface –Centerline curve –Ribbon twisting around centerline –Boundaries on two sides –Orientation

6. Ribbon Ribbon coordinate system –Distance, Offset, and loft (D,O,L) Provides a frame of reference for local spatial relationships

7. Ribbon Centerline Modeled by cubic spline Q(t)=(x(t),y(t),z(t)) Arc-length parameterization –Compute arc length s as a function of parameter t –Compute the inverse function t=A -1 (s) –Replace parameter t with A -1 (s) P(s)=(x(A -1 (s)),y(A -1 (s)),z(A -1 (s )))

8. Arc-length Parameterization Generally integral for A(t) does not integrate s=A(t)= Function t=A -1 (s) is not elementary function Numeric methods impractical for real-time applications Solution: Approximately arc-length parameterized cubic spline curve

9. Approximately Arc-length Parameterized Cubic Spline Curve Compute length of input curve Find m+1 equally spaced points on input curve Interpolate the equally space points to arc length s to derive a new cubic spline curve

10. Errors Analysis Match error –Misfit of the derived curve from an input curve –Measured by difference between the two curves at corresponding points, |Q(t)-P(s)| Arc-length parameterization error –Deviation of the derived curve from arc-length parameterization –Measured by formula

11. Experimental Results (1) m=5 (2) m=10 Experimental curve(blue) and the derived curve (red) with their knot points

12. Experimental Results (cont.) (1) m=5 (2) m=10 Match error of the derived curve

13. Experimental Results (cont.) ( 1) m=5 (2) m=10 Arc-length parameterization error of the derived curve

14. A parametric model for ribbons Through any point on a ribbon passes a line that lies on it and is perpendicular to the central axis –Intersection between the line and the central axis (x(s),y(s),z(s)) –Unit normal vector v on the line pointing to left side –A parametric surface model

15. Mapping between Ribbon and Cartesian coordinates Some computations are most naturally expressed in Cartesian coordinates (D,O,L) –Kinematics code computing object motion Other computations require object locations expressed in ribbon coordinates (X,Y,Z) –Behavior code tracking roads Efficient and robust code to map between ribbon and Cartesian coordinates

16. Mapping DOL to XYZ Compute p 1 with distance coordinate D p Compute p 2 with p 1 and offset coordinate O p Compute p with p 2 and loft coordinate L p Conclusion: this mapping is very efficient

17. Mapping XYZ to DOL Locate the closest point p 1 and get D p Compute p 2, the projection of p Offset O p is |p 1 -p 2 | Loft L p is |p-p 2 | Problem: computation of the closest point

18. Closest Point Computation Modeled as an optimization problem of computing the minimum distance between a spatial point and a parametric spatial curve –Quadratic minimization –Newton’s method –Combining quadratic minimization and Newton’s method

19. Method 1: Quadratic Minimization Let s 1, s 2, and s 3 be estimates of s * 1)Compute a quadratic polynomial p(s) that interpolates D(s) at s 1, s 2, and s 3 2)Solve s 4 that minimizes p(s) 3)if then s *  s 4 else { s i  s 4 with i such that p(s i ) = ( p(s j ) ) repeat }

20. Observation of Quadratic Minimization Rates of slow convergence and divergence make this method unacceptable by itself. Fails on seemingly simple cases. In these cases the method usually makes progress in the initial iterations and then stalls.

21. Method 2: Newton’s Method Solve the rootfinding problem Let s 0 be initial estimate of s * repeat until

22. Observation of Newton’s Method Infrequent divergence causes unacceptable failure rate. Unpredictably diverges for some points With a good initial estimate converges in 1 or 2 iterations.

23. Method 3: Combining Quadratic Minimization and Newton’s Method Exploits the complementary strengths of the two optimization techniques Run the quadratic method for a small number of steps (typically about 4). Run Newton’s method initialized with the result from the quadratic method.

24. Observation of Composite Method Reliable and rapid convergence –Quadratic method provides a good estimate to initialize Newton’s method –Newton’s method robustly converges (usually in 1 or 2 iterations.) The method has undergone rigorous testing in the Hank Simulator –We have had no failures.

25. Results of Three Methods Example curve and some spatial points Statistics of three methods

26. Intersections—Where Roads Join Shared regions of way Non-oriented Corridors splice together incoming and outgoing lanes –Seen as single lane ribbons

27. Limitations of ribbons Transition between ribbons is hard –Different ribbons represent different local coordinate systems –Hard to understand the spatial relationship of positions on different ribbons Solution: a uniform ribbon called a path to unite connected, aligned ribbons –Lanes on roads and corridors on intersections are seen ribbons

28. Path Single-lane ribbon overlaid on the road network –Easy transition between a road and an intersection An interface between behaviors and the environment –The path relates behaviors to environment Augmented dynamically –The vehicle is never behind or ahead of its path.

29. A Path as a Basis for Building Behaviors A path is a frame of reference for tracking –Aim for a succession of pursuit points on the path A frame of reference for local spatial relationships

30. Tracking Behavior Ribbon coordinates Pursuit point Project pursuit point onto the vehicle’s local XY plane Compute a circular track Move the vehicle to a new position on the circular track Project the new position onto ribbon surface

31. Cruising Behavior Determine desired speed of an vehicle Proportional controller

32. Path Based Following Behavior Query the leader on path Compute relative distance and relative speed Proportional-derivative controller Discarded if positive otherwise applied

33. Intersection Behavior Gates access to a shared region of roads – An intersection is a resource Decision of action selection – Going forward/stopping – Stop a vehicle on a desired position Right-of-way rules and social conventions embedded in environment database Regulate the motion of a vehicle before it enters an intersection

34. Intersection Behavior (Cont’) Solve deadlock problem –Two vehicles yield right of way to other two vehicles to block them at the same time Solve starvation problem –A vehicle yielding right of way gets stuck if vehicles having right of way come in a continuous stream

35. Limitations of a Path An action-oriented geometric steering guide –A path between the current and goal positions does not always exist Solution: a goal-oriented topological directional steering guide called a route

36. Route A succession of roads and intersections A global, strategic goal of an agent –The route is determined ahead of the path –The path is updated according to the requirements of the route Support lane changing behaviors –Discretional lane change (DLC) –Mandatory lane change (MLC)

37. Route Based Lane Changing Decision Making The route forms constraints for choice of lane on a road Lane change decisions subject to the constraints –A DLC must consider route constraints –An MLC must enforce route constraints

38. Path Based Lane Changing Action A lane changing gap determined by the spatial relationship between the vehicle and nearby vehicles The path forms a frame of reference to deviate the pursuit point from the current lane to the target lane

39. Behavior Combination Combine acceleration contributions from –Cruising behavior –Following behavior –Intersection behavior Combine steering angle contributions from –Tracking behavior –Lane changing behavior

40. Solve Disturbances between Component Behaviors The switch in leaders when a vehicle leaves one lane and enters another –Abrupt acceleration change –Start two copies of following behavior Following behavior stops lane changing progress –Relaxing following distance

41. Solve Disturbances between Component Behaviors (Cont.) Following behavior unnecessarily slows down lane changing process –Disable following behavior in the original lane when it has a clear trajectory to the target lane –Visibility computation in DO plane

42. Contributions 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

43. Future Work Accuracy, efficiency, and robustness of geometric computations for off-road objects Efficient model for non-oriented navigable surfaces, i.e., intersections Good pursuit point control Behavior diversity Non autonomous behaviors