Abstract
The behavior of traffic participants is full of uncertainties in the real world. It depends on their intentions, the road layout, and the interaction between them. Probabilistic intention and motion predictions are unavoidable to safely navigate in complex scenarios. In this work, we propose a framework to compute the motion prediction of the surrounding vehicles taking into account all possible routes obtained from a given map. To that end, a Dynamic Bayesian Network is used to model the problem and a particle filter is applied to infer the probability of being on a specific route and the intention to change lanes. Our framework, based on Markov chains, is generic and can handle various road layouts and any number of vehicles. The framework is evaluated in two scenarios: a two-lane highway and a three-lane merging highway. Finally, the influence of a set of lane-changing methods is evaluated on the predictions of the vehicles present on the scene.
This work has been partially funded by the Spanish Ministry of Science and Innovation, the Community of Madrid through SEGVAUTO 4.0-CM (S2018-EMT-4362) Programme, and by the European Commission and ECSEL Joint Undertaking through the Project NEWCONTROL (826653).
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Trentin, V., Artuñedo, A., Godoy, J., Villagra, J. (2022). Interaction-Aware Motion Prediction at Highways: A Comparison of Three Lane Changing Models. In: Klein, C., Jarke, M., Helfert, M., Berns, K., Gusikhin, O. (eds) Smart Cities, Green Technologies, and Intelligent Transport Systems. VEHITS SMARTGREENS 2021 2021. Communications in Computer and Information Science, vol 1612. Springer, Cham. https://doi.org/10.1007/978-3-031-17098-0_14
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