  Automated driving systems (ADS) have undergone a significant improvement in
the last years. ADS and more precisely self-driving cars technologies will
change the way we perceive and know the world of transportation systems in
terms of user experience, mode choices and business models. The emerging field
of Deep Learning (DL) has been successfully applied for the development of
innovative ADS solutions. However, the attempt to single out the best deep
neural network architecture and tuning its hyperparameters are all expensive
processes, both in terms of time and computational resources. In this work,
Bayesian Optimization (BO) is used to optimize the hyperparameters of a
Spatiotemporal-Long Short Term Memory (ST-LSTM) network with the aim to obtain
an accurate model for the prediction of the steering angle in a ADS. BO was
able to identify, within a limited number of trials, a model -- namely
BOST-LSTM -- which resulted, on a public dataset, the most accurate when
compared to classical end-to-end driving models.
