  Many trajectory forecasting methods, implementing deterministic and
stochastic models, have been presented in the last decade for automotive
applications. In this work, a deep-learning framework is proposed to model and
predict the evolution of the coupled driver-vehicle system dynamics.
Particularly, we aim to describe how the road geometry affects the actions
performed by the driver. Differently from other works, the problem is
formulated in such a way that the user may specify the features of interest.
Nonetheless, we propose a set of features that is commonly used for automotive
control applications to practically show the functioning of the algorithm. To
solve the prediction problem, a deep recurrent neural network based on Long
Short-Term Memory autoencoders is designed. It fuses the information on the
road geometry and the past driver-vehicle system dynamics to produce
context-aware predictions. Also, the complexity of the neural network is
constrained to favour its use in online control tasks. The efficacy of the
proposed approach was verified in a case study centered on motion cueing
algorithms, using a dataset collected during test sessions of a
non-professional driver on a dynamic driving simulator. A 3D track with complex
geometry was employed as driving environment to render the prediction task
challenging. Finally, the robustness of the neural network to changes in the
driver and track was investigated to set guidelines for future works.
