Eco-Driving for Connected and Automated Vehicles: Managing Uncertainty in Mixed Traffic With a Physics-Enhanced Data-Driven Method

With the growing emphasis on sustainable transportation, enhancing vehicular energy efficiency in mixed traffic scenarios has become a key focus, particularly in cooperative control strategies for Connected and Automated Vehicles (CAVs) amidst the unpredictability of Human-Driven Vehicles (HDVs). These strategies are essential for improving driving safety, traffic efficiency, and reducing energy consumption. This study introduces a novel Physics-Enhanced Data-Enabled Predictive Control (PE-DeePC) method to optimize CAV control in mixed traffic flows, where the presence of HDVs introduces significant uncertainty. By incorporating partial system physics, such as velocity and acceleration, into a unified state-space equation framework, PE-DeePC effectively combines data-driven insights with physical models, enabling optimal control decisions that are both accurate and robust. This method tackles the challenges of improving energy efficiency in the face of noisy measurements and the unpredictable behaviors of HDVs. Extensive simulation results show that the PE-DeePC achieves superior performance in robust CAV control under noisy, unpredictable conditions, and reduces energy consumption in the entire mixed traffic flow. The framework demonstrates energy reductions of 8.57%, 3.59%, and 13.17% over existing benchmark algorithms, highlighting its potential as a reliable and efficient solution for improving traffic management and energy efficiency in real-world mixed traffic scenarios.