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The Eco-Driving control problem seeks to perform fuel efficient speedplanning for a Connected and Autonomous Vehicle (CAV) that can exploitinformation available from advanced mapping, and from Vehicle-to-Everything(V2X) communication. The ability of an Eco-Driving strategy to adapt in realtime to variable traffic scenarios where surrounding vehicles can be eitherconnected or unconnected is critical for further development and deployment ofthis technology in the transportation sector. In this work, the Eco-Drivingstrategy, formulated as a receding-horizon optimal control problem, isintegrated with a target vehicle speed prediction model and solved via DynamicProgramming (DP) to determine the optimal speed trajectory in the presence of ahuman-driven target vehicle. An encoder-decoder architecture analyzes thepatterns in the target vehicle velocity recorded over a historic window using aGated-Recurrent-Unit (GRU) based encoder and generates an estimate of thefuture velocity trajectory using the GRU based decoder. A sensitivity study isdone to analyze the effect of the historical and prediction windows on theaccuracy of the velocity predictor. The proposed Eco-Driving controller isevaluated through microscopic simulations using a traffic simulator.

Eco-Driving of Connected and Autonomous Vehicles with Sequence-to-Sequence Prediction of Target Vehicle Velocity