Improved social force model considering conflict avoidance

Li, Qiaoru; Liu, Ying; Kang, Zengxin; Li, Kun; Chen, Liang

doi:10.1063/1.5132945

Cited by 19 publications

(6 citation statements)

References 37 publications

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“…They argued that pedestrians were attracted and repelled by other pedestrians, surrounding obstacles, and destinations in addition to their driving forces. Li et al [12] proposed an improved social force model that enabled pedestrians to avoid potential conflicts in advance during movement. The pedestrian movement behavior model could guide the movement of pedestrians and was applied in the field of computer vision.…”

Section: Related Workmentioning

confidence: 99%

Multi-Pedestrian Tracking in Crowded Scenes by Modeling Movement Behavior and Optimizing Kalman Filter

Jian-hong

Wang

2022

IEEE Access

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Most multi-object tracking methods have achieved good results in tracking multiple pedestrians with Kalman filter, but their tracking performance in crowded scenes is still poor due to pedestrian avoidance and frequent occlusion. In crowded scenes, the pedestrian trajectory prediction with Kalman filter alone is unreliable. In this paper, a two-dimensional field-of-view avoidance force model (AFM) is proposed to assist the Kalman filter prediction by sensing the avoidance force and then complete pedestrian tracking. In the model, each pedestrian has a two-dimensional field of view to perceive the avoidance force, which determines the next predicted trajectory. In real scenes, pedestrians tend to be more concerned about the surrounding area, so different areas are set to simulate the attention mechanisms of pedestrians in real scenes. In the FairMOT model, AFM is used to optimize the pedestrian state values of Kalman filter prediction and the optimized model is trained on the MOT16 public dataset. The experimental results on the MOT20 benchmark dataset show that compared with the mainstream tracking model FairMOT, our method respectively improves MOTA by 2.7% and IDF1 by 2.2%. Our method also achieves the good performance on MOT15, MOT16, and MOT17 tracking benchmarks.

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Section: Related Workmentioning

confidence: 99%

Multi-Pedestrian Tracking in Crowded Scenes by Modeling Movement Behavior and Optimizing Kalman Filter

Jian-hong

Wang

2022

IEEE Access

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“…None of the previous researchers is focusing on simulating the different interactions between both genders. This is because mostly in other situations there are only minor differences between both interactions [17,25]. However, in Tawaf scenario, it plays a very important part to simulate the situation precisely [36].…”

Section: Social Force Modelmentioning

confidence: 99%

An integration of enhanced social force and crowd control models for high-density crowd simulation

Kolivand

Rahim

Sunar

et al. 2020

Neural Comput & Applic

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Social force model is one of the well-known approaches that can successfully simulate pedestrians’ movements realistically. However, it is not suitable to simulate high-density crowd movement realistically due to the model having only three basic crowd characteristics which are goal, attraction, and repulsion. Therefore, it does not satisfy the high-density crowd condition which is complex yet unique, due to its capacity, density, and various demographic backgrounds of the agents. Thus, this research proposes a model that improves the social force model by introducing four new characteristics which are gender, walking speed, intention outlook, and grouping to make simulations more realistic. Besides, the high-density crowd introduces irregular behaviours in the crowd flow, which is stopping motion within the crowd. To handle these scenarios, another model has been proposed that controls each agent with two different states: walking and stopping. Furthermore, the stopping behaviour was categorized into a slow stop and sudden stop. Both of these proposed models were integrated to form a high-density crowd simulation framework. The framework has been validated by using the comparison method and fundamental diagram method. Based on the simulation of 45,000 agents, it shows that the proposed framework has a more accurate average walking speed (0.36 m/s) compared to the conventional social force model (0.61 m/s). Both of these results are compared to the real-world data which is 0.3267 m/s. The findings of this research will contribute to the simulation activities of pedestrians in a highly dense population.

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“…Examples of this stream of study include a cellular automata model of asymmetric simple exclusion process that was used to describe a situation where pedestrians walk through a crowded area to investigate how crowds may interfere with pedestrian behaviour [1]. The social force model has also been modified by introducing long-range conflict avoidance behaviours with the consideration of pedestrians' acceleration, deceleration or detour conflict avoidance strategies, and the authors concluded that conflict avoidances cannot be ignored for the interactions among pedestrians [2]. Existing simulation studies also tried to explored microscopic pedestrian movement interactions in even finer scale, e.g.…”

Section: Introductionmentioning

confidence: 99%

Experimental and modelling studies of collision avoidance strategy choices and behavioural characteristics in interweaving pedestrian flow

Luan

Liu

et al. 2022

R. Soc. open sci.

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The mechanisms of collision avoidance (CA) behaviours in interweaving pedestrian flow movements are important for pedestrian space planning and emergency management but not well understood yet. In this paper, a series of controlled interweaving pedestrian flow experiments with different densities are carried out to investigate the CA behaviours, especially CA strategy choices. Four types of CA strategies are manually identified in these experiments. Nine characteristic parameters based on the trajectory data are defined to explore the characteristics of CA behaviours. The experimental results reveal that (i) the CA behaviours change with density levels; (ii) heterogeneities can be found for individual pedestrians; (iii) the defined characteristic parameters show different statistical features for different types of CA strategies, and correlations exist between most of the parameter pairs; (iv) it usually takes 0.5–2.5 s to complete a CA process with a trajectory length of 0.5–3.5 m. A multi-nomial logit (MNL) model and a long-short-term-memory (LSTM) model are established respectively for predicting pedestrians' choices of CA strategies using the selected characteristic parameters as inputs. The modelling results prove the importance of using time-series data for pedestrian behaviour modelling, and the LSTM models show advantages over the MNL model at this point.

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Improved social force model considering conflict avoidance

Cited by 19 publications

References 37 publications

Multi-Pedestrian Tracking in Crowded Scenes by Modeling Movement Behavior and Optimizing Kalman Filter

Multi-Pedestrian Tracking in Crowded Scenes by Modeling Movement Behavior and Optimizing Kalman Filter

An integration of enhanced social force and crowd control models for high-density crowd simulation

Experimental and modelling studies of collision avoidance strategy choices and behavioural characteristics in interweaving pedestrian flow

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