Crowd Motion Detection and Prediction for Transportation Efficiency in Shared Spaces

Yang, Dongfang; Maroli, John M.; Li, Linhui; El-Shaer, Menna; Jabr, Bander A.; Redmill, Keith; Özgüner, F.; Özgüner, Ü.

doi:10.1109/scope-gctc.2018.00007

Cited by 2 publications

(4 citation statements)

References 17 publications

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“…In the dynamic equation of SFM, the motion of the pedestrian is formulated to be under the influence of multiple forces with the basic ones being an attractive force to the destination and repulsive forces from both obstacles and other agents (e.g, pedestrians or vehicles) in the environment. The summation of these forces specifies the acceleration vector of a pedestrian according to Newton's second law of motion [15], [16]. While the underlying dynamic equation of SFM with only the attraction force towards the destination can be used as a basic motion model for pedestrians, the social forces from other agents are an additional factor in the SFM for interaction modelling.…”

Section: A Expert-based Modelsmentioning

confidence: 99%

“…1) Repulsive forces: The original social force model (SFM), proposed by Helbing and Molnar (1995) [73], only considers the social interactions between pedestrians. Many papers have extended that model by introducing additional forces for modelling pedestrian-vehicle interaction [15], [74]. The way these additional forces are formulated is the main difference between these extended social force models.…”

Section: A Explicit Interaction Modelingmentioning

confidence: 99%

“…In the Social Force Model where interactions are modelled through forces, the repulsive force exerted on the target pedestrian from another pedestrian is formulated differently from the repulsive force from a vehicle [15], [70]. In some models, this is done through the selection of different values for some of the parameters in the formulation, such as the interactive strength and interaction range of the repulsive force from a vehicle compared to a pedestrian [18], [19], [74], [75].…”

Section: A Interaction Of Agents In Sfmmentioning

confidence: 99%

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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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Section: A Expert-based Modelsmentioning

confidence: 99%

Section: A Explicit Interaction Modelingmentioning

confidence: 99%

Section: A Interaction Of Agents In Sfmmentioning

confidence: 99%

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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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“…The focus in the original model was on pedestrians' social interactions. However, subsequent work has extended this model to incorporate pedestrianvehicle interactions [68,69]. These extensions propose additional forces to account for such interactions.…”

Section: Explicit Interaction Modelingmentioning

confidence: 99%

Holistic Spatio-Temporal Graph Attention for Trajectory Prediction in Vehicle–Pedestrian Interactions

Alghodhaifi,

Lakshmanan

2023

Sensors

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Ensuring that intelligent vehicles do not cause fatal collisions remains a persistent challenge due to pedestrians’ unpredictable movements and behavior. The potential for risky situations or collisions arising from even minor misunderstandings in vehicle–pedestrian interactions is a cause for great concern. Considerable research has been dedicated to the advancement of predictive models for pedestrian behavior through trajectory prediction, as well as the exploration of the intricate dynamics of vehicle–pedestrian interactions. However, it is important to note that these studies have certain limitations. In this paper, we propose a novel graph-based trajectory prediction model for vehicle–pedestrian interactions called Holistic Spatio-Temporal Graph Attention (HSTGA) to address these limitations. HSTGA first extracts vehicle–pedestrian interaction spatial features using a multi-layer perceptron (MLP) sub-network and max pooling. Then, the vehicle–pedestrian interaction features are aggregated with the spatial features of pedestrians and vehicles to be fed into the LSTM. The LSTM is modified to learn the vehicle–pedestrian interactions adaptively. Moreover, HSTGA models temporal interactions using an additional LSTM. Then, it models the spatial interactions among pedestrians and between pedestrians and vehicles using graph attention networks (GATs) to combine the hidden states of the LSTMs. We evaluate the performance of HSTGA on three different scenario datasets, including complex unsignalized roundabouts with no crosswalks and unsignalized intersections. The results show that HSTGA outperforms several state-of-the-art methods in predicting linear, curvilinear, and piece-wise linear trajectories of vehicles and pedestrians. Our approach provides a more comprehensive understanding of social interactions, enabling more accurate trajectory prediction for safe vehicle navigation.

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Crowd Motion Detection and Prediction for Transportation Efficiency in Shared Spaces

Cited by 2 publications

References 17 publications

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

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

Holistic Spatio-Temporal Graph Attention for Trajectory Prediction in Vehicle–Pedestrian Interactions

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