What is the current state of the art in this project area?
There has been a great deal of work in the pedestrian dynamics community in devising models for pedestrian motion and interaction.
The research has largely focused on three approaches: cellular automata (CA), social forces (SF)
and reciprocal velocity obstacles.
1. Cellular Automata
These techniques aim to work by discretizing the simulation domain into a grid of cells. An agent may occupy at most one cell and a cell
may contain no more than a single agent. Pedestrians move from cell to cell based on various techniques. These approaches were quite
common in simulating vehicular traffic. Blue and Adler first applied these techniques to pedestrians using a set of crafted rules for
determining pedestrian movement and showed that they were able to reproduce observable pedestrian phenomena in both uni-directional
and bi-directional flows [3, 4]. Other approaches applied probabilistic techniques to determine agent
movement and augmented the functionality with scalar and vector fields to reproduce emergent phenomena . CA-based
approaches tend to exhibit some artefacts due to the discretization of the space. There has been extensive research to understand and
mitigate these effects. Kirchner et al. examined the impact of the size of the cells . Maniccam investigated the
impact of exchanging square cells for hexagonal cells . Yamamoto et al. introduced the real-coded cellular automata"
to minimize the aliasing artifacts which arise from traversing a rectilinear grid . CA-based approaches remain an active
branch of research; these methods have been used as a basis to simulate complex scenarios with more elaborate pedestrian behaviors.
2. Social Forces Models
These models treat pedestrians like mass-particles and applies Newtonian-like physics to evolve the simulation. Helbing introduced the first
such model in which each agent was motivated by a driving force which served to lead the agent towards a goal . The
agent would interact with obstacles and agents in the environment through the superpositioning of repulsive forces. Later SF-based models
explored alternate formulations of the repulsive forces or novel forces including, compression and friction forces for evacuation
, relative-velocity-dependent forces [13, 14], and group formations
. A more complete overview can be found in . SF-based models have been shown to exhibit
self-organizing behaviors .
3. Velocity Obstacle Models
Agent interactions are based on velocity obstacles (VO). One agent denies a set of velocities for another agent which would lead to an
inevitable collision. Agents then select a velocity outside of this set for a collision-free path. Originally, each agent assumed that all
other agents were non-responsive . Later VO-based models introduced alternative VO formulations which assumed that agents
were aware that those around them would also respond [18, 19, 1]. Pedestrian simulation
using VO-based models has been shown to exhibit self-organizing behaviors and varying flow through a bottleneck  as well as
accurate microscopic interactions between individual pedestrians .
Recently, Curtis et. al. proposed the notion of “intention filters” which can thought of as a third cognitive level between global and local
planners. Architecturally, it sits at the interface between the global and local components. Its purpose is to adapt the intention (i.e.
preferred velocity) to local dynamic conditions. It does this by using reasoning whose scope is on the order of 100 seconds. An intention
ﬁlter affords greater control over agent crowd behavior. The algorithms used by the global and local planners inherently encode a particular
space of behaviors. These behaviors can be tuned by tweaking parameters and, in some cases, the algorithms can be extended to incorporate
additional parameters and behaviors. Ultimately, these efforts are constrained by the underlying models used. By inserting an intention
ﬁlter, one can provide a new model which can complement the global and local planners’ models. Working in conjunction, this new system
can produce behaviors which may otherwise be impossible to achieve with the global and local planners alone.
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