… we propose a probabilistic prediction approach … drivers involving both discrete and continuous
driving decisions. Based on this, the distribution over all future trajectories of the predicted …

… Learned probabilistic and interaction-aware driver behavior models iteratively applied to
generate possible scene predictions (colors indicate prediction horizon of up to 7 s). The green …

… a probabilistic framework to overcome this limitation using the primitives of driving behavior.
… Different from research in [38]–[40] with the aim to predict driver behaviors, this paper will …

… 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 …

… 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 …

… 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 …