Adaptive Eco-Driving With Guided Speed Planning and Lane Changing Through Signalized Intersections

In dynamic traffic flow conditions, lane-changing maneuvers hold significant potential for achieving energy and time efficiency. However, existing research often overlooks the influence of a global reference speed trajectory, especially in urban settings with multiple signalized intersections. To address this gap, this study proposes an eco-driving strategy for connected and automated vehicles (CAVs) that integrates deep reinforcement learning (DRL) and model predictive control (MPC), considering the impact of a guided speed profile with good synergy. A three-stage speed planning framework, following a coarse-smooth-optimization manner, is introduced to efficiently generate an energy-saving guided speed profile. After that, the guided speed profile is delivered into DRL, serving as a network input together with information on surrounding human driver vehicles (HDVs). The DRL adopts a soft actor-critic (SAC) algorithm integrating with an MPC controller, which can generate control outputs for both lane-changing and car-following maneuvers based on the DRL decisions and guided speed profile. Additionally, the MPC also certifies decisions for vehicle safety. Simulation results indicate that compared to benchmark methods, the proposed strategy achieves energy savings of up to 23.6% while maintaining high computational efficiency, which is accompanied by a smaller time delay approaching intersections and ensuring vehicle safety.