Intersection eco-driving for automated vehicles: SMPC-based strategies for handling leading vehicle starting-up uncertainties

Driving at intersections often differs from regular car -following scenarios, particularly in terms of start-up speed uncertainties. Extracting useful information from these uncertainties is crucial for improving traffic and energy efficiency, ultimately achieving eco-driving goals for automated vehicles. However, traditional car -following models are inadequate for describing intersection driving behaviors, while current eco-driving strategies often lack a thorough consideration of intersection traffic uncertainties. To this end, a new eco-driving strategy based on stochastic model predictive control (SMPC) is proposed, specifically addressing uncertainties in the starting -up behavior of leading vehicle (LV). To begin, a naturalistic driving dataset is constructed for a queuing scenario at a signalized intersection, enabling the establishment of probabilistic models for LV starting -up behavior. Next, an SMPC-based motion planning algorithm is introduced to facilitate optimal control of the ego vehicle (EV), directly incorporating the LV's uncertainties. Specifically, a phantom LV model is introduced to depict LV's starting -up probability in its initial preparing stage, and then in the accelerating stage a gaussian process regression (GPR) approach is employed for more accurate prediction of LV acceleration. Then the algorithm's effectiveness is validated through stochastic and playback simulations in SUMO and Matlab. The playback simulations using conditions from naturalistic driving demonstrate that the algorithm significantly enhances energy efficiency, achieving savings of 21% and 37% compared to electric and fueled vehicles in real -world scenarios, respectively. Surprisingly, despite expectations, the algorithm can also improve traffic efficiency by an average of 17.9% and 13.6% for electric and fueled vehicles, respectively. Finally, the road experiment validates that our algorithm can achieve total savings of 15.8% energy in our experimental electric vehicle compared to real drivers. This algorithm has the potential to serve as an effective eco-driving solution for automated vehicle longitudinal planning and control in urban settings.