Real-time Collision Avoidance for Pedestrian and Bicyclist Simulation: A Smooth and Predictive Approach
Author(s) -
Jocelyn Buisson,
Stéphane Galland,
Nicolas Gaud,
Mikaël Gonçalves,
Abderrafìâa Koukam
Publication year - 2013
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2013.06.108
Subject(s) - crowds , collision avoidance , computer science , pedestrian , crowd simulation , obstacle , collision , obstacle avoidance , simulation , collision detection , oscillation (cell signaling) , artificial intelligence , robot , computer security , mobile robot , transport engineering , engineering , biology , law , political science , genetics
This article introduces a new collision avoidance model enabling the design of efficient realistic virtual pedestrian and cyclist behaviors. It is a force-based model using collision prediction with dynamic time-windows to predict future potential collisions with obstacles and other individuals. It introduces a new type of force called sliding force to allow a smooth avoidance of potential collisions while enabling the pedestrian to continue to progress towards its goal. Unlike most existing models, our forces are not scaled according to the distance to the obstacle but depending on the estimate of the collision time with this obstacle. This inherently integrates obstacles’ velocity. This greatly reduces the compu- tational complexity of the model while ensuring a smooth avoidance. This model is oscillation-free except for concave obstacles. It enables the reproduction of inherent emergent properties of real crowds such as spontaneous organizations of pedestrians into lane lines, etc. This model is computationally efficient and designed for real time simulation of large crowds
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