Adapting the Social Force Model for Low Density Crowds in Open Environments

Prédhumeau, Manon; Dugdale, Julie; Spalanzani, Anne

doi:10.1007/978-3-030-61503-1_49

Cited by 5 publications

(5 citation statements)

References 14 publications

(38 reference statements)

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“…The model proposed in Section 3 has been implemented in C++ using Pedsim ros (Okal et al, 2014). Pedsim ros is an open source crowd simulator that was adapted to implement the SFM described by Prédhumeau et al (2019Prédhumeau et al ( , 2020, and integrates a set of software libraries and tools to develop robot applications.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, a small random force represents variations that allow the agents not to have too rigid behavior. For these forces, we use a modified version of the SFM that we previously developed to take into account individuals in open environments like shared spaces (Prédhumeau et al, 2019). The proposed version considers the visual perception and attention of pedestrians, and the adaptations of their personal space depending on the crowd density around them.…”

Section: General Approachmentioning

confidence: 99%

“…For pedestrian-pedestrian interactions, the SFM and its parameters are the ones used by Prédhumeau et al (2019), based on the work of Moussaïd et al (2009), for individual pedestrians, and by Prédhumeau et al (2020), based on the work of Moussaïd et al (2010), for pedestrian groups. Parameters values have been previously calibrated in these works and are kept here.…”

Section: Sfm Calibrationmentioning

confidence: 99%

See 2 more Smart Citations

Agent-Based Modeling for Predicting Pedestrian Trajectories Around an Autonomous Vehicle

Prédhumeau¹,

Mancheva²,

Dugdale³

et al. 2022

jair

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This paper addresses modeling and simulating pedestrian trajectories when interacting with an autonomous vehicle in a shared space. Most pedestrian–vehicle interaction models are not suitable for predicting individual trajectories. Data-driven models yield accurate predictions but lack generalizability to new scenarios, usually do not run in real time and produce results that are poorly explainable. Current expert models do not deal with the diversity of possible pedestrian interactions with the vehicle in a shared space and lack microscopic validation. We propose an expert pedestrian model that combines the social force model and a new decision model for anticipating pedestrian–vehicle interactions. The proposed model integrates different observed pedestrian behaviors, as well as the behaviors of the social groups of pedestrians, in diverse interaction scenarios with a car. We calibrate the model by fitting the parameters values on a training set. We validate the model and evaluate its predictive potential through qualitative and quantitative comparisons with ground truth trajectories. The proposed model reproduces observed behaviors that have not been replicated by the social force model and outperforms the social force model at predicting pedestrian behavior around the vehicle on the used dataset. The model generates explainable and real-time trajectory predictions. Additional evaluation on a new dataset shows that the model generalizes well to new scenarios and can be applied to an autonomous vehicle embedded prediction.

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Section: Methodsmentioning

confidence: 99%

Section: General Approachmentioning

confidence: 99%

Section: Sfm Calibrationmentioning

confidence: 99%

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Agent-Based Modeling for Predicting Pedestrian Trajectories Around an Autonomous Vehicle

Prédhumeau¹,

Mancheva²,

Dugdale³

et al. 2022

jair

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“…The simulator integrates social models of pedestrian behavior in shared spaces [33]. The visual perception and Fig.…”

Section: B Spaciss Simulatormentioning

confidence: 99%

How To Evaluate the Navigation of Autonomous Vehicles Around Pedestrians?

Kabtoul,

Prédhumeau,

Spalanzani

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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The navigation of autonomous vehicles around pedestrians is a key challenge when driving in urban environments. It is essential to test the proposed navigation system using simulation before moving to real-life implementation and testing. Evaluating the performance of the system requires the design of a diverse set of tests which spans the targeted working scenarios and conditions. These tests can then undergo a process of evaluation using a set of adapted performance metrics. This work addresses the problem of performance evaluation for an autonomous vehicle in a shared space with pedestrians. The methodology for designing the test simulations is discussed. Moreover, a group of performance metrics is proposed to evaluate the different aspects of the navigation: the motion safety, the quality of the generated trajectory and the comfort of the pedestrians surrounding the vehicle. Furthermore, the success/fail criterion for each metric is discussed. The implementation of the proposed evaluation method is illustrated by evaluating the performance of a pre-designed proactive navigation system using a shared space crowd simulator under Robot Operating System (ROS).

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“…people) and their comfort, naturalness and sociability [9]. There are several approaches to solve the SRN problem that are based on: Artificial Potential Field (APF) [10], [11], Deep Reinforcement Learning (DRL) [12], Fig. 1: The Pepper robot navigates a crowded hospital-like environment simulated with Gazebo and PedsimROS.…”

Section: Introductionmentioning

confidence: 99%

Online Social Robot Navigation in Indoor, Large and Crowded Environments

Silva,

Verdezoto,

Paillacho

et al. 2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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New robotics applications require robots to complete tasks in social spaces (i.e. environments shared with people), thus arising the necessity of enabling robots to operate in a socially acceptable manner. Some social spaces tend to be large and crowded (e.g. museums, shopping malls), which require robots to move around while showing appropriate social behaviors (e.g. not interfering with human's comfortable areas). Moving under such conditions is generally called social robot navigation, and there are different approaches to do so. Nonetheless, current approaches are mostly limited to navigate large and outdoor spaces, where both robots and people can easily avoid each other. Other approaches have been tested in indoor environments, however, the test environments tend to be small and largely empty. In this paper, we present an online social robot navigation framework, which allow robots to navigate indoor, large and crowded environments, while showing social behaviors. Our framework consists of 3 modules: 1) world modeling that incorporates a novel Social Heatmap (SH) to represent crowded areas, 2) multilayered path planning that uses sampling-based approaches, and 3) path following control. We extensively benchmark our approach against stateof-the-art approaches in challenging simulated scenarios, and we also demonstrate its feasibility with the Pepper robot in real-world trials.

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Adapting the Social Force Model for Low Density Crowds in Open Environments

Cited by 5 publications

References 14 publications

Agent-Based Modeling for Predicting Pedestrian Trajectories Around an Autonomous Vehicle

Agent-Based Modeling for Predicting Pedestrian Trajectories Around an Autonomous Vehicle

How To Evaluate the Navigation of Autonomous Vehicles Around Pedestrians?

Online Social Robot Navigation in Indoor, Large and Crowded Environments

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