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• Diw shortest distance between P and wall• Ds 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

http://www.mysocalledsensorylife.com/?p=2021

• The collision occurs when another pedestrian Pj comes in the personal space of Pi at time tc

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

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

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.)

• http://gamma.cs.unc.edu/PLE/pubs/PLE.pdf• http://d.wanfangdata.com.cn/periodical_zggd

xxxswz-jsjkx201003011.aspx• http://people.cs.uu.nl/ioannis/publications.ht

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