Developing an Adaptive Strategy for Connected Eco-Driving under Uncertain Traffic Condition

The eco-approach and departure (EAD) application for signalized intersections has been proved to be environmentally efficient in a Connected and Automated Vehicles (CAVs) system. The traffic and signal phase and timing (SPaT) information transmitted from the roadside equipment unit, vehicle equipped sensors (e.g. radars) and other connected vehicles are the main inputs to the existing algorithms. However, due to the limitation of the communication and sensing range, it is too late to start eco-driving until preceding traffic is fully detected. Instead, the historical data, such as queue length distribution may be applied to developing a robust speed profile that enables eco-driving to start in an early stage. In this paper, a two-phase iterative approach is developed with the use of historical queue distribution. A graph-based model is created with nodes representing states of the host vehicle and traffic condition, and directed edges with weight representing expected energy consumption between two connected states. The shortest path is calculated that minimizes the total energy consumption for vehicles approaching a pre-timed signalized intersection. Numerical simulations have shown that the proposed method is robust and adaptive to varying traffic and queue conditions, and could achieve around 9% energy savings compared to other baseline methods.