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Predictive Collision Avoidance Model for Enhanced Pedestrian Simulation

This presentation explores a novel predictive collision avoidance model designed for pedestrian simulation, presented by Ioannis Karamouzas and team. The method leverages force field approaches to predict potential collisions, allowing for smooth, energy-efficient, and visually pleasing pedestrian motions. It iteratively computes evasive forces, accommodating complex environments. The presentation covers previous works, the proposed method, implementation details, experimental assessments, and future developments in group path planning, highlighting advancements since the introduction of the model.

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Predictive Collision Avoidance Model for Enhanced 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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