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A predictive Collision Avoidance Model for Pedestrian Simulation

A predictive Collision Avoidance Model for Pedestrian Simulation. Author : Ioannis Karamouzas et al. Presented by : Jessica Siewert. Content of presentation. Previous work The method Implementation Experiments Assessment Developments since . Introduction – Previous work.

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A predictive Collision Avoidance Model for Pedestrian Simulation

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  1. A predictive Collision Avoidance Model for Pedestrian Simulation Author: IoannisKaramouzas et al. Presentedby: Jessica Siewert

  2. Content of presentation • Previous work • The method • Implementation • Experiments • Assessment • Developments since

  3. Introduction – Previous work • Dynamic potential-field approach (too general) • Corridor-Map-Method • Helbing Social Force Fields • Example-based (too expensive)

  4. Introduction – Now we want… • Anticipation and prediction (so in advance) • Deal with large and cluttered environments • No constant change of orientation, pushing each other and moving back/forth

  5. Introduction – We got… • Reynolds unaligned collision avoidance • => Feurtey predicts potential collisions within time and resolves by adapting speed and trajectory • => Paris et al. Anticipative model to steer • Shao and Terzopoulos: Reactive routines to determine avoidance maneuvers.

  6. Introduction – We got… • Van den Berg Reciprocal Velocity Obstacle • Pettré et al. Egocentric model for local collision avoidance

  7. Introduction – Our method… • Based on force field approach • Early avoidance hypothesis, anticipation/prediction • Energy-efficient motions • Less curved paths • Smooth natural flow • Oscillation-free

  8. Introduction – Contributions… • Force field method based (Shao, Berg, Pettré don’t) • Easier in formulation and implementation • Faster, able to handle thousands • Calculated differently producing better looking results (visually pleasing, smoothly avoiding)

  9. The method – Overview • Pedestrian Interactions • => Pedestrian Simulation Model • Collision Avoidance

  10. The method – PedestrianInteractions • Scanning and Externalization • Personal Space • Principle of Least Effort

  11. The method – Pedestrian Sim. Model • Modeled as little cylinders with radius r • The pedestrian tries to reach its goal • The goal is pulling the pedestrian towards itself with a goal force

  12. The method – Pedestrian Sim. Model • The pedestrian wants to move at a certain speed • It reaches this spreed gradually over time

  13. The method – Pedestrian Sim. Model • All the walls act on the pedestrian repulsively • Diw shortest distance between P and wall • Ds safe discance P likes from the wall

  14. The method – Pedestrian Sim. Model • A pedestrian keeps a distance from others to feel comfortable (“Personal space”) • Modeled as a disc with radius p>r (is varied) http://www.mysocalledsensorylife.com/?p=2021

  15. The method – Pedestrian Sim. Model • The collision occurs when another pedestrian Pj comes in the personal space of Pi at time tc

  16. The method – Pedestrian Sim. Model • A pedestrian has an anticipation time (can vary) • Collisions within this time are actively avoided • To simulate this an evasive Force is applied

  17. Collision avoidance • Collision prediction

  18. Collision avoidance • Selecting pedestrians • Sorted on increasing collision time • Keep the first 2 to 5

  19. Collision avoidance • Avoidance maneuvers

  20. Collision avoidance • Computing the evasive Force • Weighted sum of N forces • OR • Iterative approach! Agile101.net

  21. Implementation • Efficient Collision Prediction • Anticipation time • Iterative approach • Vary p, r, v and t • Maximum distance

  22. Implementation • Adding variation • Noise Force • Time integration • Simulation time steps • Sum of forces • Orientation

  23. Experiments – Claim recall • Anticipation/prediction based • Easier in formulation and implementation • Faster, able to handle thousands • Energy-efficient motions • Less curved paths • Smooth natural flow • Oscillation-free • Visually pleasing/natural looking

  24. Movies… • file:///C:/Users/Jessica/Downloads/Circle.avi • file:///C:/Users/Jessica/Downloads/Scene0.avi • file:///C:/Users/Jessica/Downloads/Scene1.avi • file:///C:/Users/Jessica/Downloads/Scene2.avi • file:///C:/Users/Jessica/Downloads/Scene3.avi • file:///C:/Users/Jessica/Downloads/park.avi • file:///C:/Users/Jessica/Downloads/crosswalks.avi

  25. Assessment – promises • Scanning and externalization? • Natural looking? • Easy implementation: extendability?

  26. Assessment – method • Reasoningthatleadsto smart pedestrianselection • Reasoningthatleads to iterativeapproach • Howwouldthismethod combine withobstacleavoidancemethods?

  27. Assessment – experiments • 25% of CPU usage? • What about the high-cluttered environments? • How is the time step chosen?

  28. Assessment – results • Swirl effect • Up front anticipation results in no interaction • No ellipse-shaped personal space needed?

  29. Assessment – shortcomings • No couples or coherent groups • No cultural, cognitive or psychological factors • Nothing like the reciprocal method

  30. Developments since then • Path Planning for Groups Using Column Generation (Marjan van den Akker, Roland Geraerts e.a.) • http://gamma.cs.unc.edu/PLE/pubs/PLE.pdf • http://d.wanfangdata.com.cn/periodical_zggdxxxswz-jsjkx201003011.aspx • http://people.cs.uu.nl/ioannis/publications.html

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