Lane-Changing-Enabled Eco Approach Control for Autonomous Vehicles at a Signalized Intersection in Mixed Traffic Environment

Abstract

In complex urban traffic environments, vehicle-to-everything technology can reduce energy consumption and optimize travel time for vehicles at a signalized intersection. We propose a lane-changing-enabled eco approach control strategy that considers constraints from surrounding vehicles, traffic lights, and queues ahead. Our strategy adopts a hierarchical receding horizon control framework that provides the connected and autonomous vehicle with optimal lane and speed planning for adapting to dynamic traffic environment. In the upper level, we set up virtual traffic light for each lane via a mixed vehicle platoon model, utilizing their signal phase time constraints and constraints of surrounding vehicles to formulate a mixed-integer nonlinear programming problem to obtain optimal lane and speed reference. In the lower level, a car-following model is conducted in time domain and the reference speed is put into the controller to obtain the optimal velocity. Numerical experiment results show that our strategy is superior to regular eco-approach and departure strategy and lane-changing-enabled car-following strategy in terms of energy consumption reduction in the traffic scenarios without lane changing. In the traffic scenarios with lane changing, our strategy improves traffic efficiency by reducing travel time.