… The present work is based on a probabilistic prediction approach using Bayesian networks
which contain random variables connected by conditional probabilities, as visualized in Fig. 4…

… Driver behavior recognition and vehicle trajectory prediction … Widely used probabilistic
models include Hidden Markov … and make probabilistic predictions for their future behaviors. …

… driver acceleration profiles. This paper studies the effectiveness of recurrent neural networks
in predicting … baseline methods in replicating driver behavior, including smoothness and …

… In this paper, a novel probabilistic prediction for interactive traffic scenario was proposed.
The approach was rooted in the optimal transport problem and meaningful from the information …

… a probabilistic model for estimating and predicting the behavior of drivers immersed in traffic…
The model is defined within a stochastic filtering framework and estimation and prediction …

… In [18], seven different regression methods for interaction-aware microscopic driver behavior
are learned and compared to each other in highway scenarios. An ANN based mapping …

… and goals of a driver in order to predict his behavior. A fully probabilistic approach is presented
… Dillmann, “A probabilistic model for estimating driver behaviors and vehicle trajectories in …

… A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic
environments. In Proceedings of the 13th International IEEE Conference on Intelligent …

… Dillmann, “A probabilistic model for estimating driver behaviors and vehicle trajectories in
traffic environments,” in 2010 IEEE International Conference on Intelligent Transportation …

… to train the State Prediction Model (SPM); (3… driver behavior with the help of SPM; and (4)
quantitative analysis, wherein we analyze the uncertain behavior of the driver with probabilistic …