Reactive Pedestrian Path Following from Examples

1. Reactive Pedestrian Path Following from Examples Computer Animation and Social Agents 2003 Ronald A. Metoyer Jessica K. Hodgins

2. Introduction • Need a system to model the movement of many people walking and interacting • Want to maintain control over the path each individual takes • Hard to deal with collision avoidance with many characters • Easy to use

3. Previous Work • Reynolds • Boid Model for flocks, schools, and herds • Pedestrian Models • Fluid flow model • Inter-pedestrian interaction models (Helbing and Molnar) • Social interaction based on + and – potential fields • Lane formation in halls, queuing, turn taking

4. 2D Character Intelligence • Exploit fact that humans have to move on a 2D plane (for the most part) • Basic level of intelligence • Reactive path following, obstacles, other pedestrians • Social Forces Model • Reactive control utilizes potential fields • Obstacles are repulsive • Goals are attractive

5. Potential Fields

6. Modeling • Point mass dynamics • Update equation is: • Where the force fx is obtained from the potential field • dt is the simulation steps • m is the mass of the character • Although goal locations can be specified, it is desirable to allow a definable path for the character to follow • People are experts in drawing a path through a scene in the absence of moving obstacles • Can also be generated through automatic process

7. Path Diagram • User draws a spline path for character to follow • The path is converted into forces by the following: • Character will attempt to follow the direction of the path, but as it gets more off track, it’ll be pulled back stronger

8. Direction Primitives • Intelligence model will produce correct 2D animation in terms of obstacle avoidance, but not necessarily natural looking • Alert user to potential collisions and ask how to resolve them • Navigation Primitives • Yield, Cut-in-front, Go-around-right, Go-around-left, No-action • Chosen based on traffic planning research

9. Direction Primitives (Cont.) • Focus on two tasks a pedestrian performs • Monitoring • Observing other pedestrians in the area to determine their navigational intents • Yielding • Act of adjusting velocity (Magnitude or Direction) to avoid a potential collision

10. Learning • Use previous direction primitive choices to aid the user in future decisions • Direction Primitive • Feature vector that describes current scene • Is the path around left blocked by other pedestrians or obstacles (Y or N) • Is the path around right blocked by other pedestrians or obstacles (Y or N) • Relative speed of the colliding pedestrian (5) • Approach direction of the colliding pedestrian (8) • Colliding pedestrian’s distance to collision (5) • Pedestrian’s distance to collision (5) • Desired travel direction (3)

11. Learning (Cont.) • Naïve Bayes Classifier • Five primitives are hypotheses • Seven variables are inputs • Potential collisions are classified into one of the 5 primitives • Advantages • Outperforms neural networks and machine learning algorithms in most real life cases • Disadvantages • Limited by the fact that it can only deal with discrete data

12. 3D Motion Generation • Use motion capture • Create a directed graph of poses to get a probability matrix for transitions from one pose to another

13. Results • Compared the Naïve Bayes algorithm to actual choices made by users • Claim 72% accuracy as opposed to a random choice which would be 20% naturally • This doesn’t mean much, because all it is really testing is their ability to train a Bayes classifier

14. Limitations • Requires (utilizes) a lot of human intervention • There is no motion capture data of a person stopped, so it appears the person is spinning around when standing still

15. Videos

16. Basic System U/I Desired State State Controller Dynamics Torques State State Torques Integrator Renderer State

18. Cart / Pole

19. Cart / Pole • Apply torque to cart’s wheels • Balance pole • Accomplish desired location • Accomplish desired velocity • Extra Credit • Swing-up task

20. Basic input file for Cart / Pole • language = C • gravity = 0 0 -9.80665 • prefix = cartpole • # cart is a truck-sized object, 20 x 4 x 3 feet = 6x1.5x1 meters • # with car-like density of 170 kg / m^3 • # therefore, truck-like mass of 1800kg = 4000 lbs

21. body = cart joint = slider jname = pos • mass = 1530 • inertia = 414.37500000 4717.50000000 4876.87500000 • bodyToJoint = 0 0 0 • pin = 1 0 0

22. # A 300 lb = 136 kg ladder that is roughly # 15 x 1.5 x 0.5 feet = 4.6x.45x.15 meters • body = ladder inboard = cart joint = pin jname = theta • mass = 52.785 • inertia = 0.98971875 93.17652187 93.96829687 • bodyTojoint = -2.3 0 0 • inbToJoint = -3.0 0 0.75 • pin = 0 1 0

23. Swinger • More complicated simulation of girl on a swing • Hands are rigidly attached to rope • Butt is rigidly attached to seat • You control torques at shoulder, elbow, hips, and knee

24. Swinger • State machine • Swinging has discrete modes, or states • Define when they begin and end • Define what movements are required for each state

25. Discrete event simulations • Very important!!! • Each simulation has a simulation timestep, DT • Smaller timestep required for larger forces • Numerical imprecision of integrator • Make sure your simulations are precise by dropping DT by an order of magnitude and confirm behavior is the same