Heterogeneous Crowd Simulation

Janapalli, Srivastav R.; Hesham, Omar; Wainer, Gabriel

doi:10.23919/springsim.2019.8732873

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…We introduced extensions to the CPD model for crowd modeling and simulation that address the addition of vehicles, motorcycles, and obstacles entities into a single system, making it heterogeneous. 49 We added the Minkowski sum approach, flexibility for adding foreign objects and simulating traffic scenarios. The developed engine displays potential to simulate multiple scenarios of vehicle and crowd interactions.…”

Section: Discussionmentioning

confidence: 99%

Advanced models for centroidal particle dynamics: short-range collision avoidance in dense crowds

Hesham

Wainer

2021

SIMULATION

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Computer simulation of dense crowds is finding increased use in event planning, congestion prediction, and threat assessment. State-of-the-art particle-based crowd methods assume and aim for collision-free trajectories. That is an idealistic yet not overly realistic expectation, as near-collisions increase in dense and rushed settings compared with typically sparse pedestrian scenarios. Centroidal particle dynamics (CPD) is a method we defined that explicitly models the compressible personal space area surrounding each entity to inform its local pathing and collision-avoidance decisions. We illustrate how our proposed agent-based method for local dynamics can reproduce several key emergent dense crowd phenomena at the microscopic level with higher congruence to real trajectory data and with more visually convincing collision-avoidance paths than the existing state of the art. We present advanced models in which we consider distraction of the pedestrians in the crowd, flocking behavior, interaction with vehicles (ambulances, police) and other advanced models that show that emergent behavior in the simulated crowds is similar to the behavior observed in reality. We discuss how to increase confidence in CPD, potentially making it also suitable for use in safety-critical applications, including urban design, evacuation analysis, and crowd-safety planning.

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Section: Discussionmentioning

confidence: 99%

Advanced models for centroidal particle dynamics: short-range collision avoidance in dense crowds

Hesham

Wainer

2021

SIMULATION

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“…The interaction between a vehicle and pedestrians in very close contact is also modelled with these repulsive forces in [71], [78]. In a simulation case of an ambulance trying to pass through a crowded scene of pedestrians, a collision penalty force is exerted whenever the vehicle and the pedestrians penetrate into each other's personal space.…”

Section: A Explicit Interaction Modelingmentioning

confidence: 99%

“…The other difference in the interaction formulation of a Social Force Model is caused by considering the difference in geometry and size of the vehicle compared to a pedestrian, which affects the calculation of the distance between the two agents [17], [72], [75]. The size of the agent also impacts the personal space (e.g., the space around each agent into which any encroachment feels uncomfortable) [71], [78] and conflict zone (e.g., A distance below which there is a risk of collision) [68], [77] of a pedestrian versus a vehicle. While pedestrians are basically considered to have a circular shape in SFM, vehicles are commonly modelled as an ellipse.…”

Section: A Interaction Of Agents In Sfmmentioning

confidence: 99%

Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review

Golchoubian

Ghafurian

Dautenhahn

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Planning an autonomous vehicle's (AV) path in a space shared with pedestrians requires reasoning about pedestrians' future trajectories. A practical pedestrian trajectory prediction algorithm for the use of AVs needs to consider the effect of the vehicle's interactions with the pedestrians on pedestrians' future motion behaviours. In this regard, this paper systematically reviews different methods proposed in the literature for modelling pedestrian trajectory prediction in presence of vehicles that can be applied for unstructured environments. This paper also investigates specific considerations for pedestrian-vehicle interaction (compared with pedestrian-pedestrian interaction) and reviews how different variables such as prediction uncertainties and behavioural differences are accounted for in the previously proposed prediction models. PRISMA guidelines were followed. Articles that did not consider vehicle and pedestrian interactions or actual trajectories, and articles that only focused on road crossing were excluded. A total of 1260 unique peer-reviewed articles from ACM Digital Library, IEEE Xplore, and Scopus databases were identified in the search. 64 articles were included in the final review as they met the inclusion and exclusion criteria. An overview of datasets containing trajectory data of both pedestrians and vehicles used by the reviewed papers has been provided. Research gaps and directions for future work, such as having more effective definition of interacting agents in deep learning methods and the need for gathering more datasets of mixed traffic in unstructured environments are discussed.

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Reinforcement Learning for Crowd Control Optimization with An A* Based Social Force Interaction Model

Liang,

Wei,

et al. 2023

2023 13th International Conference on Information Science and Technology (ICIST)

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Heterogeneous Crowd Simulation

Cited by 3 publications

References 19 publications

Advanced models for centroidal particle dynamics: short-range collision avoidance in dense crowds

Advanced models for centroidal particle dynamics: short-range collision avoidance in dense crowds

Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review

Reinforcement Learning for Crowd Control Optimization with An A* Based Social Force Interaction Model

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