  The Eco-Driving control problem seeks to perform fuel efficient speed
planning for a Connected and Autonomous Vehicle (CAV) that can exploit
information available from advanced mapping, and from Vehicle-to-Everything
(V2X) communication. The ability of an Eco-Driving strategy to adapt in real
time to variable traffic scenarios where surrounding vehicles can be either
connected or unconnected is critical for further development and deployment of
this technology in the transportation sector. In this work, the Eco-Driving
strategy, formulated as a receding-horizon optimal control problem, is
integrated with a target vehicle speed prediction model and solved via Dynamic
Programming (DP) to determine the optimal speed trajectory in the presence of a
human-driven target vehicle. An encoder-decoder architecture analyzes the
patterns in the target vehicle velocity recorded over a historic window using a
Gated-Recurrent-Unit (GRU) based encoder and generates an estimate of the
future velocity trajectory using the GRU based decoder. A sensitivity study is
done to analyze the effect of the historical and prediction windows on the
accuracy of the velocity predictor. The proposed Eco-Driving controller is
evaluated through microscopic simulations using a traffic simulator.
