A multi-layer social force approach to model interactions in shared spaces using collision prediction

Rinke, Nils; Schiermeyer, Chris; Pascucci, Federica; Berkhahn, Volker; Friedrich, Bernhard

doi:10.1016/j.trpro.2017.05.144

Cited by 49 publications

(31 citation statements)

References 12 publications

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“…In their work, the 3N algorithm generates a crowd motion network from which similar motion patterns are detected using the concept of coherent neighbour invariance. A different approach called Hybrid Social Influence Model (HSIM) was proposed by Ullah et al [10], which used a density-independent version of the Social Force Model [14], [15] to model crowd motion and Communal model [16] to group similar motion patterns. The topic models which are popular in language processing, have been used by Chen et al [17] for group detection.…”

Section: Related Workmentioning

confidence: 99%

Scene-Independent Motion Pattern Segmentation in Crowded Video Scenes Using Spatio-Angular Density-Based Clustering

2020

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Motion pattern segmentation for crowded video scenes is an open problem because of the inability of existing approaches to tackle unpredictable crowd behaviour across varied scenes. To address this problem, we propose a Spatio-Angular Density-based Clustering (SADC) approach, which performs motion pattern segmentation by clustering the spatial and angular information obtained from the input trajectories. The knearest neighbours of each trajectory and the angular deviation between trajectories constitute the spatial and angular information, respectively. Effective integration of the spatio-angular information with an improvised density-based clustering algorithm makes this approach scene-independent. The performance of most clustering algorithms in the literature is parameter-driven. Choosing a single parameter value for different types of scenes decreases the overall clustering performance. In this paper, we have shown that our approach is robust to scene changes using a single threshold, and, through the analysis of parameters across eight commonly occurring crowded scenarios, we point out the range of thresholds that are suitable for each scene category. We evaluate the proposed approach on the benchmarked CUHK dataset. The experimental results show the superior clustering performance and execution speed of the proposed approach when compared to the state-of-the-art over different scene categories.

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Section: Related Workmentioning

confidence: 99%

Scene-Independent Motion Pattern Segmentation in Crowded Video Scenes Using Spatio-Angular Density-Based Clustering

2020

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“…Traffic microsimulation is a well-established field of research, and commercial software products exist that permit traffic simulations that are accurate on the scale of a large junction or a city centre, for example, to predict how a range of alternative road infrastructure designs will affect traffic throughput [23,24]. The road user behaviour models in these traffic simulations are, however, not designed to capture the details of local interactions, and this underdeveloped area is now garnering increasing attention, with some modellers approaching it from a traffic microsimulation starting point [26,27], and others addressing it as a data-driven machine learning challenge [28,29,30].…”

Section: Neurobiologically-informed Mathematical Modelsmentioning

confidence: 99%

How Do We Study Pedestrian Interaction with Automated Vehicles? Preliminary Findings from the European interACT Project

Merat

Lee

Markkula

et al. 2019

Lecture Notes in Mobility

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This paper provides an overview of a set of behavioural studies, conducted as part of the European project interACT, to understand road user behaviour in current urban settings. The paper reports on a number of methodologies used to understand how humans currently interact in urban traffic, in order to establish what information would be useful for the design of future AVs, when interacting with other road users, especially pedestrians. In addition to summarising the results from a number of observation studies, we report on preliminary results from Virtual Reality studies, investigating if, in the absence of a human vehicle controller, externally presented interfaces can be used for communication between AVs and pedestrians. Finally, an overview of the mathematical and computational modelling techniques used to understand how AV and pedestrian behaviour can be both cooperative, and effective is provided. The hope is that future AVs can be designed with an understanding of how humans cooperate and communicate in mixed traffic, promoting good traffic flow, user acceptance and user trust.

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“…The social forces are a measure representing the motivation of the individual particle to carry out certain movements, and consider the inuence of the other particles surrounding it. Rinke et al [32] describe that the modeling of social forces is based on Newton's second law (NSL) of motion. According to the NSL, the state of an object will change in the presence of an external force.…”

Section: Retaining the Motile Particlesmentioning

confidence: 99%

A hybrid social influence model for pedestrian motion segmentation

Ullah

Uzair

2018

Neural Comput & Applic

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We propose a hybrid social inuence model (HSIM) which is a novel and automatic method for pedestrian motion segmentation. One of the major attractions of the HSIM is its capability to handle motion segmentation when the pedestrian ow is randomly distributed. In the proposed HSIM, we rst extract the motion information from the input video through particle initialization and optical ow. The particles are then examined to keep only the signicant and nonstationary particles. To detect consistent segments, we adopt the communal model (CM) that models the inuence of particles on each other. The CM infers inuence from uncorrelated behaviors among particles and models the eect that particle interactions have on the spread of social behaviors. Finally, the detected segments are rened to eliminate the eects of oversegmentation. We perform extensive experiments on four benchmark datasets and compare the results with two baseline and four state-of-the-art motion segmentation methods. Our results show that HSIM achieves superior pedestrian motion segmentation and outperforms the compared methods in terms of both Jaccard Similarity Metric (JSM) and F-score.

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A multi-layer social force approach to model interactions in shared spaces using collision prediction

Cited by 49 publications

References 12 publications

Scene-Independent Motion Pattern Segmentation in Crowded Video Scenes Using Spatio-Angular Density-Based Clustering

Scene-Independent Motion Pattern Segmentation in Crowded Video Scenes Using Spatio-Angular Density-Based Clustering

How Do We Study Pedestrian Interaction with Automated Vehicles? Preliminary Findings from the European interACT Project

A hybrid social influence model for pedestrian motion segmentation

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