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

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

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.

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

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

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)

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

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

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

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

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

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

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

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

Collision avoidance Collision prediction

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

Avoidance maneuvers Collision avoidance

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

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

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

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

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

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

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

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

Developments since then Path Planning for Groups Using Column Generation (Marjan van den Akker, Roland Geraerts e.a.) xxxswz-jsjkx aspx xxxswz-jsjkx aspx ml ml