  In a typical car-following scenario, target vehicle speed fluctuations act as
an external disturbance to the host vehicle and in turn affect its energy
consumption. To control a host vehicle in an energy-efficient manner using
model predictive control (MPC), and moreover, enhance the performance of an
ecological adaptive cruise control (EACC) strategy, forecasting the future
velocities of a target vehicle is essential. For this purpose, a deep recurrent
neural network-based vehicle speed prediction using long-short term memory
(LSTM) and gated recurrent units (GRU) is studied in this work. Besides these,
the physics-based constant velocity (CV) and constant acceleration (CA) models
are discussed. The sequential time series data for training (e.g. speed
trajectories of the target and its preceding vehicles obtained through
vehicle-to-vehicle (V2V) communication, road speed limits, traffic light
current and future phases collected using vehicle-to-infrastructure (V2I)
communication) is gathered from both urban and highway networks created in the
microscopic traffic simulator SUMO. The proposed speed prediction models are
evaluated for long-term predictions (up to 10 s) of target vehicle future
velocities. Moreover, the results revealed that the LSTM-based speed predictor
outperformed other models in terms of achieving better prediction accuracy on
unseen test datasets, and thereby showcasing better generalization ability.
Furthermore, the performance of EACC-equipped host car on the predicted
velocities is evaluated, and its energy-saving benefits for different
prediction horizons are presented.

Systems and Control;Machine Learning;Systems and Control