Predicting Lead Vehicle Velocity for Eco-Driving in the Absence of V2V Information

WCX SAE World Congress Experience
Authors Abstract
Accurately predicting the future behavior of the surrounding traffic, especially the velocity of the lead vehicle is important for optimizing the energy consumption and improve the safety of Connected and Automated Vehicles (CAVs). Several studies report methods to predict short-to-mid-length lead vehicle velocity using stochastic models or other data-driven techniques, which require availability of extensive data and/or Vehicle-to-Vehicle (V2V) communication. In the absence of connectivity, or in data-restricted cases, the prediction must rely only on the measured position and relative velocity of the lead vehicle at the current time. This paper proposes two velocity predictors to predict short-to-mid-length lead vehicle velocity. The first predictor is based on a Constant Acceleration (CA) with an augmented stop mode. The second one is based on a modified Enhanced Driver Model (EDM-LOS) with line-of-sight feature. Both predictors rely only on information on the present values of lead vehicle position and velocity to compute a future velocity estimate. An analysis is done to compare the prediction accuracy of the proposed predictors with different experimental driving data recorded using an OBD2 scanner plugged into a passenger vehicle. Finally, the predicted lead vehicle velocity is utilized to formulate time-gap constraints for the eco-driving optimal control problem, solved via Model Predictive Control (MPC). The energy savings of the considered velocity predictors are evaluated by performing a large-scale simulation study. The proposed velocity predictor provides closest energy savings to a wait-and-see solution for a CAV in absence of V2V communication.
Meta TagsDetails
LAKSHMANAN, V., Gupta, S., D'Alessandro, S., Spano, M. et al., "Predicting Lead Vehicle Velocity for Eco-Driving in the Absence of V2V Information," Advances and Current Practices in Mobility 5(6):2278-2287, 2023,
Additional Details
Apr 11, 2023
Product Code
Content Type
Journal Article