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A predictive Collision Avoidance Model for Pedestrian Simulation Author: Ioannis Karamouzas et al. Presented by: Jessica Siewert

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

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

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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

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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.

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

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

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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)

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

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The method – Pedestrian Interactions Scanning and Externalization Personal Space Principle of Least Effort

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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

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

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All the walls act on the pedestrian repulsively D iw shortest distance between P and wall D s safe discance P likes from the wall The method – Pedestrian Sim. Model

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

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The collision occurs when another pedestrian P j comes in the personal space of P i at time t c The method – Pedestrian Sim. Model

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

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Collision avoidance Collision prediction

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Collision avoidance Selecting pedestrians – Sorted on increasing collision time – Keep the first 2 to 5

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Avoidance maneuvers Collision avoidance

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

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

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Implementation Adding variation – Noise Force Time integration – Simulation time steps – Sum of forces – Orientation

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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

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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 file:///C:/Users/Jessica/Downloads/crosswalks.avi

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

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Assessment – method Reasoning that leads to smart pedestrian selection Reasoning that leads to iterative approach How would this method combine with obstacle avoidance methods?

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

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

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

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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_zggd xxxswz-jsjkx201003011.aspx http://d.wanfangdata.com.cn/periodical_zggd xxxswz-jsjkx201003011.aspx http://people.cs.uu.nl/ioannis/publications.ht ml http://people.cs.uu.nl/ioannis/publications.ht ml

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