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In a typical car-following scenario, target vehicle speed fluctuations act asan external disturbance to the host vehicle and in turn affect its energyconsumption. To control a host vehicle in an energy-efficient manner usingmodel predictive control (MPC), and moreover, enhance the performance of anecological adaptive cruise control (EACC) strategy, forecasting the futurevelocities of a target vehicle is essential. For this purpose, a deep recurrentneural 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) modelsare discussed. The sequential time series data for training (e.g. speedtrajectories of the target and its preceding vehicles obtained throughvehicle-to-vehicle (V2V) communication, road speed limits, traffic lightcurrent and future phases collected using vehicle-to-infrastructure (V2I)communication) is gathered from both urban and highway networks created in themicroscopic traffic simulator SUMO. The proposed speed prediction models areevaluated for long-term predictions (up to 10 s) of target vehicle futurevelocities. Moreover, the results revealed that the LSTM-based speed predictoroutperformed other models in terms of achieving better prediction accuracy onunseen test datasets, and thereby showcasing better generalization ability.Furthermore, the performance of EACC-equipped host car on the predictedvelocities is evaluated, and its energy-saving benefits for differentprediction horizons are presented.

Deep Learning-Based Vehicle Speed Prediction for Ecological Adaptive Cruise Control in Urban and Highway Scenarios