Modeling pedestrian–vehicle mixed-flow in a complex evacuation scenario

Zhang, Zhao; Fu, Daocheng

doi:10.1016/j.physa.2022.127468

Cited by 8 publications

(8 citation statements)

References 33 publications

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“…From a broad perspective, these interaction effects can be categorized to be modelled either explicitly or implicitly. In the explicit interaction modelling approaches (e.g., see [17], [18], [70], [71]), the effect of the vehicle on a pedestrian's motion is forced through some clear terms in the formulation of the pedestrian's motion such as through explicit forces in the social force model (e.g., [68], [72]). On the other hand, datadriven modules often account for these interactions implicitly by using the vehicle's trajectory as another input to the model along with the target pedestrian's own trajectory (e.g., see [30], [45]).…”

Section: Rq2: Interaction Modelingmentioning

confidence: 99%

“…In these models, the asymmetrical effect of the vehicle's influence is accounted for in the anisotropy function or considering the approach angle and its change rate [68], [77]. On the other hand, some others have modelled a vehicle with an ellipse and considered the asymmetry of the vehicle's influence by calculating the pedestrian's relative distance based on its angular position with respect to the ellipse [17], [72], [75].…”

Section: A Explicit Interaction Modelingmentioning

confidence: 99%

“…Others have used a fixed ellipse which encloses the vehicles and repulsive force is modelled as an exponential function of the distance between the vehicle and the pedestrian, where the distance depends on the relative orientation of the pedestrian with respect to the vehicle [17], [72], [75]. Yang et al, (2018) define three areas around the vehicle -namely the front area, body area, and back area -and propose a different formulation for the lateral and longitudinal forces of the vehicle on the pedestrian in each area [76].…”

Section: A Explicit Interaction Modelingmentioning

confidence: 99%

“…Other heuristics such as TTC have also been used in combination with the Social force model [72], [77] to keep track of interaction between pedestrians and vehicles. In [72] the TTC governs the principal behind the vehicles behaviour in interaction with pedestrians, where the vehicle breaks whenever the TTC is lower than a threshold. In [77] another decision layer on top of the SFM gets activated when the TTC between the agents gets below a threshold.…”

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.…”

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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