Overcoming driving challenges in complex urban traffic: A multi-objective eco-driving strategy via safety model based reinforcement learning

This study proposes a novel eco-driving control strategy for connected and automated hybrid electric vehicles, which utilizes deep reinforcement learning (DRL) to optimize various aspects of driving performance, including fuel economy, ride comfort, and travel efficiency, in complex urban traffic scenarios. The proposed strategy incorporates a driving safety model that predicts potential risk associated with the DRL agent's planned speed, thus ensuring the safety of the DRL based eco-driving strategy. Additionally, we propose a multi-objective composite reward function design scheme that considers various constraints caused by traffic elements, such as traffic lights, preceding vehicles, road curvature, and speed limit. This design scheme enables the proposed strategy to effectively adapt to diverse driving challenges in complex urban traffic scenarios. To evaluate the proposed strategy, we develop an urban traffic simulation model based on real-world road and traffic data from Shanghai, China. This model is used as the test scenario and can reflect real urban traffic conditions. The simulation results demonstrate the capability of the proposed strategy to safely and efficiently control vehicles to complete driving tasks in complex urban scenarios. Moreover, the proposed strategy excels in simultaneously optimizing the driving comfort and fuel consumption of the controlled vehicle.