Deep reinforcement learning-based eco-driving control for connected electric vehicles at signalized intersections considering traffic uncertainties

Eco-driving control poses great energy-saving potential at multiple signalized intersection scenarios. However, traffic uncertainties can often lead to errors in ecological velocity planning and result in increased energy consumption. This study proposes an eco-driving approach with a hierarchical framework to be leveraged at signalized intersections that considers the impact of traffic uncertainty. The proposed approach leverages a queue-based traffic model in the upper level to estimate the impact of traffic uncertainty and generate dynamic modified traffic light information. In the lower level, a deep reinforcement learning-based controller is constructed to optimize velocity subject to the constraints from the traffic lights and traffic uncertainty, thereby reducing energy consumption while ensuring driving safety. The effectiveness of the proposed control strategy is demonstrated through numerous simulation case studies. The simulation results show that the proposed method significantly improves energy economy and prevents unnecessary idling in uncertain traffic scenarios, as compared to other approaches that ignore traffic uncertainty. Furthermore, the proposed method is adaptable to different traffic scenarios and showcases energy efficiency.

Automated summary

This study proposes an eco-driving approach with a hierarchical framework to be leveraged at signalized intersections considering the impact of traffic uncertainty. The proposed method utilizes a queue-based traffic model to estimate traffic uncertainty and generate dynamic modified traffic light information. Additionally, a deep reinforcement learning-based controller is constructed to optimize velocity, reducing energy consumption and ensuring driving safety. Simulation results demonstrate the effectiveness of the proposed control strategy in improving energy economy and preventing unnecessary idling in uncertain traffic scenarios, as compared to approaches that ignore traffic uncertainty. The proposed method is also adaptable to different traffic scenarios and exhibits energy efficiency.