A predictive Collision Avoidance
Model for Pedestrian Simulation
Author: Ioannis Karamouzas et al.
Presented by: Jessica Siewert
Content of presentation
Previous work
The method
Developments since
Introduction – Previous work
Dynamic potential-field approach (too general)
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
• => Paris et al. Anticipative model to steer
• Shao and Terzopoulos: Reactive routines to
determine avoidance maneuvers.
Introduction – We got…
• Van den Berg Reciprocal Velocity Obstacle
• Pettré et al. Egocentric model for local
collision avoidance
Introduction – Our method…
• Based on force field approach
• Early avoidance hypothesis,
• 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 method – Pedestrian Sim. Model
• The pedestrian wants to move at a certain
• It reaches this spreed gradually over time
The method – Pedestrian Sim. Model
• All the walls act on the pedestrian repulsively
• D shortest distance between P and wall
• D 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
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
Collision avoidance
• Collision prediction
Collision avoidance
• Selecting pedestrians
– Sorted on increasing collision time
– Keep the first 2 to 5
Collision avoidance
• Avoidance maneuvers
Collision avoidance
• Computing the evasive Force
– Weighted sum of N forces
– OR
– Iterative approach!
• Efficient Collision Prediction
– Anticipation time
– Iterative approach
– Vary p, r, v and t
– Maximum distance
• 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
Assessment – promises
• Scanning and externalization?
• Natural looking?
• Easy implementation: extendability?
Assessment – method
• Reasoning that leads to smart pedestrian
• 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.)

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