A Connectivity-Based Real-Time Traffic Prediction Considering Lane-Changing Maneuvers with Application to Eco-Driving Control of Electric Vehicles

Connected vehicles (CVs) exchange information with other CVs and intelligent infrastructure, enhancing traffic state predictions like speed and density. This aids in developing planning and control algorithms for CVs, including eco-driving controls that rely on future traffic conditions. However, predicting traffic states in real-time for the next 10–15 s is challenging, especially with mixed traffic conditions containing CVs and an unknown number of human-driven vehicles (HVs). Lane changes further complicate the problem, especially around vehicles equipped with eco-driving controllers. The slow driving speed of eco-driving vehicles before intersections during red signal phases may prompt other HVs to overtake them, resulting in more frequent lane changes. To address this, we integrate a macroscopic traffic flow model with a microscopic car-following model for traffic prediction. The Payne-Whitham (PW) model is modified to account for the impact of lane changes on the evolution of traffic states. An unscented Kalman filter (UKF) is used to estimate traffic states ahead of the ego vehicle using information from CVs. The algorithm then propagates the PW model to predict future traffic states and computes the trajectory of the immediate preceding vehicle based on these predictions. Using these results, we formulate an eco-driving co-optimization problem for connected and autonomous electric vehicles (CAEVs), taking into account vehicle dynamics, powertrain operation and battery operation. Traffic scenarios from both Simulation of Urban MObility (SUMO) and real-world road tests are used to evaluate the performance of our approach. Results show a reduction in traffic prediction error of up to 35.95% and an improvement in energy efficiency for the ego vehicle by up to 13.14%. Furthermore, our approach consistently benefits the ego vehicle under different CV penetration rates.