  This article presents a holistic approach for probabilistic cyclist intention
detection. A basic movement detection based on motion history images (MHI) and
a residual convolutional neural network (ResNet) are used to estimate
probabilities for the current cyclist motion state. These probabilities are
used as weights in a probabilistic ensemble trajectory forecast. The ensemble
consists of specialized models, which produce individual forecasts in the form
of Gaussian distributions under the assumption of a certain motion state of the
cyclist (e.g. cyclist is starting or turning left). By weighting the
specialized models, we create forecasts in the from of Gaussian mixtures that
define regions within which the cyclists will reside with a certain
probability. To evaluate our method, we rate the reliability, sharpness, and
positional accuracy of our forecasted distributions. We compare our method to a
single model approach which produces forecasts in the form of Gaussian
distributions and show that our method is able to produce more reliable and
sharper outputs while retaining comparable positional accuracy. Both methods
are evaluated using a dataset created at a public traffic intersection. Our
code and the dataset are made publicly available.
