Minimal predicted distance: A common metric for collision avoidance during pairwise interactions between walkers

Olivier, Anne-Hélène; Marin, Antoine; Crétual, Armel; Pettré, Julien

doi:10.1016/j.gaitpost.2012.03.021

Cited by 108 publications

(146 citation statements)

References 16 publications

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“…While the large-τ behavior in our datasets is therefore dominated by screening, we can use the largest observed values of t 0 to place a lower bound estimate on the intrinsic range of unscreened interactions (which we denote as τ 0 ). This estimate suggests that an appropriate value is τ 0 ≈ 3 s, which is consistent with previous research demonstrating an interaction time horizon of 2 − 4 s [21].…”

supporting

confidence: 91%

“…First, each individual in a crowd experiences a complex environment of competing forces, making it difficult to isolate and robustly quantify a single pairwise interaction. Secondly, a pedestrian's motion is strongly influenced not just by the present position of neighboring pedestrians, but by their anticipated future positions [17][18][19][20][21], a fact which has influenced recent models [22][23][24][25]. Consider, for example, two well-separated pedestrians walking into a head-on collision (Fig.…”

mentioning

confidence: 99%

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Universal Power Law Governing Pedestrian Interactions

2014

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Human crowds often bear a striking resemblance to interacting particle systems, and this has prompted many researchers to describe pedestrian dynamics in terms of interaction forces and potential energies. The correct quantitative form of this interaction, however, has remained an open question. Here, we introduce a novel statistical-mechanical approach to directly measure the interaction energy between pedestrians. This analysis, when applied to a large collection of human motion data, reveals a simple power law interaction that is based not on the physical separation between pedestrians but on their projected time to a potential future collision, and is therefore fundamentally anticipatory in nature. Remarkably, this simple law is able to describe human interactions across a wide variety of situations, speeds and densities. We further show, through simulations, that the interaction law we identify is sufficient to reproduce many known crowd phenomena.In terms of its large-scale behaviors, a crowd of pedestrians can look strikingly similar to many other collections of repulsively-interacting particles [1][2][3][4]. These similarities have inspired a variety of pedestrian crowd models, including cellular automata and continuum-based approaches [5][6][7][8], as well as simple particle or agent-based models [9][10][11][12][13][14][15]. Many of these models conform to a longstanding hypothesis that humans in a crowd interact with their neighbors through some form of "social potential" [16], analogous to the repulsive potential energies between physical particles. How to best determine the quantitative form of this interaction potential, however, has remained an open question, with most previous researchers employing a simulation-driven approach.Previously, direct measurement of the interaction law between pedestrians has been confounded by two primary factors. First, each individual in a crowd experiences a complex environment of competing forces, making it difficult to isolate and robustly quantify a single pairwise interaction. Secondly, a pedestrian's motion is strongly influenced not just by the present position of neighboring pedestrians, but by their anticipated future positions [17][18][19][20][21], a fact which has influenced recent models [22][23][24][25]. Consider, for example, two well-separated pedestrians walking into a head-on collision (Fig. 1a). These pedestrians typically exhibit relatively large acceleration as they move to avoid each other, as would result from a large repulsive force. On the other hand, pedestrians walking in parallel directions exhibit almost no acceleration, even when their mutual separation is small (Fig. 1b).Here, we address both of the aforementioned factors using a data-driven, statistical mechanics-based analysis that accounts properly for the anticipatory nature of human interactions. This approach allows us to directly and robustly measure the interaction energy between pedestrians. The consistency of our measurements across a variety of settings suggests a simple and u...

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confidence: 91%

mentioning

confidence: 99%

Universal Power Law Governing Pedestrian Interactions

2014

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“…In particular, work on visual aspects of streetscape behavior is finding considerable interest in biology (and computational biology), where it has proven useful in relating gaze and the acquisition of environmental information to ideas about how humans and animals use visual flow to process environmental stimuli [68,69]. Thanks in large part to the development of motion capture technology alongside eye-tracking systems, new work is also being performed in the intellectual space between vision and locomotion that connects action and response to issues of neurological pathways [92,93,210,238], timing, and ordering in cognition [239][240][241], to motor impairment as a function of memory loss [242], and other related issues.…”

Section: Biologymentioning

confidence: 99%

“…Aspects of the weighting that occurs in estimating collision potential involves spacing and timing of movement [371], and the affordances that individual people associate with the things that they see around them [20], including the information conveyed by gait and body language [372]. Much of this information is inaccurate in human sensing, however, as are people's estimates of the relative influence of conflicting factors [240], and inaccuracies may grow more cumbersome as we age [373,374], or when we encounter things that are unfamiliar [375,376]. Visual information is also often highly dependent upon the state and position of the person doing the looking, and in this regard gaze has been shown to be of particular importance [238,371,377]; indeed, this component of vision is now readily testable using eye tracking technology and has therefore received considerable attention [9].…”

Section: Visionmentioning

confidence: 99%

“…Olivier et al [96] have shown that roles may be important in determining this over small bundles of space and time, and that these take on signaling schemes that intertwine with the positional data that are briefly exchanged and estimated as a person determines steering. Reciprocity may assume varying form, depending on different strategies, spacing, and timing when steering for animate and inanimate colliders [240].…”

Section: Steering To Avoid and Avail Of Interactionmentioning

confidence: 99%

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Computational Streetscapes

Torrens

2016

Computation

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Abstract:Streetscapes have presented a long-standing interest in many fields. Recently, there has been a resurgence of attention on streetscape issues, catalyzed in large part by computing. Because of computing, there is more understanding, vistas, data, and analysis of and on streetscape phenomena than ever before. This diversity of lenses trained on streetscapes permits us to address long-standing questions, such as how people use information while mobile, how interactions with people and things occur on streets, how we might safeguard crowds, how we can design services to assist pedestrians, and how we could better support special populations as they traverse cities. Amid each of these avenues of inquiry, computing is facilitating new ways of posing these questions, particularly by expanding the scope of what-if exploration that is possible. With assistance from computing, consideration of streetscapes now reaches across scales, from the neurological interactions that form among place cells in the brain up to informatics that afford real-time views of activity over whole urban spaces. For some streetscape phenomena, computing allows us to build realistic but synthetic facsimiles in computation, which can function as artificial laboratories for testing ideas. In this paper, I review the domain science for studying streetscapes from vantages in physics, urban studies, animation and the visual arts, psychology, biology, and behavioral geography. I also review the computational developments shaping streetscape science, with particular emphasis on modeling and simulation as informed by data acquisition and generation, data models, path-planning heuristics, artificial intelligence for navigation and way-finding, timing, synthetic vision, steering routines, kinematics, and geometrical treatment of collision detection and avoidance. I also discuss the implications that the advances in computing streetscapes might have on emerging developments in cyber-physical systems and new developments in urban computing and mobile computing.

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The Fokker–Planck Framework in the Modeling of Pedestrians’ Motion

Borzì

2020

Crowd Dynamics, Volume 2

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Minimal predicted distance: A common metric for collision avoidance during pairwise interactions between walkers

Cited by 108 publications

References 16 publications

Universal Power Law Governing Pedestrian Interactions

Universal Power Law Governing Pedestrian Interactions

Computational Streetscapes

The Fokker–Planck Framework in the Modeling of Pedestrians’ Motion

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