Pedestrian trajectory prediction by deep-learning
Classical operational pedestrian models rely on physical, social, or psychological factors. They are defined by basic rules or generic functions depending on the environment locally. These models are specified by few parameters adjusting the dynamics and that can be interpreted. Nowadays, machine learning and data-based algorithms such as arti ficial neural networks (ANN) are used for pedestrian trajectory prediction. Potential applications are pedestrian movement in complex situations, including evacuations, autonomous vehicles, or motion planning of robots in crowded environments.
- R. Korbmacher, H. Dang and A. Tordeux, "Predicting pedestrian trajectories at different densities: A multi-criteria empirical analysis", Physica A: Statistical Mechanics and its Applications, vol. 634, pp. 129440, 2024.
- R. Korbmacher and A. Tordeux, "Toward better pedestrian trajectory predictions: the role of density and time-to-collision in hybrid deep-learning algorithms", Sensors, vol. 24, no. 7, pp. 2356, 2024.
- R. Korbmacher and A. Tordeux, "Deep Learning for Predicting Pedestrian Trajectories in Crowds" in Intelligent Systems and Applications, Arai, Kohei, Eds. Cham: Springer Nature Switzerland, 2024, pp. 720-725.
- H. Dang, R. Korbmacher, A. Tordeux, B. Gaudou and N. Verstaevel, "TTC-SLSTM: Human Trajectory Prediction Using Time-to-Collision Interaction Energy" in 2023 15th IEEE International Conference on Knowledge and Systems Engineering (KSE), 2023, pp. 1-6.
- R. Subaih, M. Maree, A. Tordeux and M. Chraibi, "Questioning the anisotropy of pedestrian dynamics: An empirical analysis with artificial neural networks", Applied Sciences, vol. 12, no. 15, pp. 7563, 2022. MDPI.
- R. Korbmacher and A. Tordeux, "Review of pedestrian trajectory prediction methods: Comparing deep learning and knowledge-based approaches", IEEE Transactions on Intelligent Transportation Systems, vol. 23(12), pp. 24126-24144, 2022. IEEE.
- A. Tordeux, M. Chraibi, A. Seyfried and A. Schadschneider, "Prediction of pedestrian dynamics in complex architectures with artificial neural networks", Journal of Intelligent Transportation Systems, vol. 24, no. 6, pp. 556-568, 2019. Taylor & Francis.
- A. Tordeux, M. Chraibi, A. Seyfried and A. Schadschneider, "Artificial neural networks predicting pedestrian dynamics in complex buildings" in Workshop on Stochastic Models, Statistics and their Application, 2019, pp. 363-372.
- A. Tordeux, M. Chraibi, A. Seyfried and A. Schadschneider, "Prediction of pedestrian speed with artificial neural networks" in International Conference on Traffic and Granular Flow, 2019, pp. 327-335.
Further references
G Saporta. Models for Understanding versus Models for Prediction. COMPSTAT 2008, pp. 315-322, 2008. Presentation
A Alahi, K Goel, V Ramanathan, A Robicquet, L Fei-Fei, S Savarese. Social LSTM: Human trajectory prediction in crowded spaces. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 961-971, 2016.
A Gupta, J Johnson, L Fei-Fei, S Savarese, A Alahi. Social GAN: Socially acceptable trajectories with generative adversarial networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2255-2264, 2018.
Here are a poster and presentation about the prediction of pedestrian speed using artificial neural networks.
