Estimating pedestrian intentions from trajectory data

Šikudová, Elena; Malinovská, Kristína; Škoviera, Radoslav; Skovierová, Júlia; Uller, Miroslav; Hlaváč, Václav

doi:10.1109/iccp48234.2019.8959707

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…They adhere to a machine learning method which allows data classification considering both numerical and categorical variables [43]. Also, they have been used for classification of pedestrian trajectory data [44]- [45]. Decision trees are built during the training process.…”

Section: Methodological Componentsmentioning

confidence: 99%

A Data-Driven Model for Pedestrian Behavior Classification and Trajectory Prediction

Papathanasopoulou

Spyropoulou

Perakis

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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Pedestrian modeling remains a formidable challenge in transportation science due to the complicated nature of pedestrian behavior and the irregular movement patterns. To this extent, accurate and reliable positioning technologies and techniques play a significant role in the pedestrian simulation studies. The objective of this research is to predict pedestrian movement in various perspectives utilizing historical trajectory data. The study features considered in this research are pedestrian class, speed and position. The ensemble of these features provides a thorough description of pedestrian movement prediction, whilst contributes to the context of pedestrian modeling and Intelligent Transportation Systems. More specifically, pedestrian movement is grouped into different classes considering gender, walking pace and distraction by employing random forest algorithms. Then, position and speed prediction is computed employing suitable data-driven methods, in particular, the locally weighted regression (LOESS method), taking into account the individual pedestrian"s profile. An LSTM-based (Long Short-Term Memory) model is also applied for comparison. The methodology is applied on pedestrian trajectory data that were collected in a controlled experiment undertaken at the Campus of the National Technical University of Athens (NTUA), Greece. Prediction of pedestrian"s movement is achieved, yielding satisfactory results.

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Section: Methodological Componentsmentioning

confidence: 99%

A Data-Driven Model for Pedestrian Behavior Classification and Trajectory Prediction

Papathanasopoulou

Spyropoulou

Perakis

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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show abstract

“…Using motion contour histograms of oriented gradients combined with a SVM they estimated the pedestrian crossing intent. Using high-level pre-processed data (e.g., position, velocity, orientation), several ML algorithms are used in [48] to train different classifiers to estimate the intention of a pedestrian to cross at a zebra crossing. Four features are derived from the original dataset and used in the classification task: (i) distance to zebra crossing, (ii) distance between the nearest road border point and the pedestrian position, (iii) distance between the nearest road point and the zebra anchor, measured along the border curve and the angle between a pedestrian's current heading direction, and (iv) pedestrian-to-zebra direction.…”

Section: Infrastructure Sensorsmentioning

confidence: 99%

“…In this paper, we propose a model based on an RF that predicts pedestrian crossing intention at different locations around an urban intersection (i.e., crosswalk, pedestrian walking in parallel to the road and a pedestrian walking on other arms of the intersection), not just a predefined ROI. Random forests is a well-established algorithm for statistical learning and has been applied to classification and recently for classification of pedestrian trajectories [48,52,53] and intention [5,51]. We also investigate the addition of a derived categorical feature, which we have called crossing, to the original dataset by performing a semantic segmentation of the environment (i.e., partition the image into two segments: sidewalk and road), and evaluate ten crossing and ten non-crossing trajectories.…”

Section: Infrastructure Sensorsmentioning

confidence: 99%

Pedestrian Crossing Intention Forecasting at Unsignalized Intersections Using Naturalistic Trajectories

Moreno

Denny

Ward

et al. 2023

Sensors

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Interacting with other roads users is a challenge for an autonomous vehicle, particularly in urban areas. Existing vehicle systems behave in a reactive manner, warning the driver or applying the brakes when the pedestrian is already in front of the vehicle. The ability to anticipate a pedestrian’s crossing intention ahead of time will result in safer roads and smoother vehicle maneuvers. The problem of crossing intent forecasting at intersections is formulated in this paper as a classification task. A model that predicts pedestrian crossing behaviour at different locations around an urban intersection is proposed. The model not only provides a classification label (e.g., crossing, not-crossing), but a quantitative confidence level (i.e., probability). The training and evaluation are carried out using naturalistic trajectories provided by a publicly available dataset recorded from a drone. Results show that the model is able to predict crossing intention within a 3-s time window.

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Interaction of automated driving systems with pedestrians: challenges, current solutions, and recommendations for eHMIs

et al. 2021

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Estimating pedestrian intentions from trajectory data

Cited by 4 publications

References 13 publications

A Data-Driven Model for Pedestrian Behavior Classification and Trajectory Prediction

A Data-Driven Model for Pedestrian Behavior Classification and Trajectory Prediction

Pedestrian Crossing Intention Forecasting at Unsignalized Intersections Using Naturalistic Trajectories

Interaction of automated driving systems with pedestrians: challenges, current solutions, and recommendations for eHMIs

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