## 1 Introduction
Similarly, the framework proposed by [PERSON] et al. [32] encountered the same problem when the simplified action space leads to a local optimum solution. To address this issue, [PERSON] et al. [33] introduced a deep deterministic policy gradient (DDPG) approach to control the ego agent with continuous acceleration. Furthermore, a twin delayed deep deterministic policy gradient (TD3) algorithm-based automated eco-driving method is reported by [PERSON] et al. [34], while TD3 is an improved version of DDPG. Although the adaptive control can be implemented in these DRL-based approaches, the accurate SPaT information is taken as part of the state of the agent. As these approaches have tested the effectiveness of the DRL-based control in a fixed timing situation [35; 36], they cannot be directly applied in the ATSC environment. Furthermore, to the best of the authors' knowledge, all of the previous DRL-based eco-driving system works lack explicability, which means that we cannot know why the ego vehicle accelerates or decelerates in every specific time step. It is obvious that the black-box logic may give rise to some safety-related issues and have a negative impact on reliable control. In this paper, we focus on electric vehicles, which attract the public's attention due to its eco-friendly feature, benefitting from the advancements of battery-related research [37; 38]. We also develop a Markov Decision Process (MDP) model for the eco-driving problem in the ATSC traffic environment to fill the previous technical gap. The optimal speed profile is generated by the Proximal Policy Optimization (PPO) algorithm, which is capable of solving continuous action space problems. Meanwhile, SHapley Additive exPlanations (SHAP) [39] is used to explain the decision process of the agent (i.e., the ego vehicle) to make the DRL algorithm-based approach explainable. The main contributions of our paper are summarized as follows:  
1. A general MDP for eco-driving control is established, whereas the reward function of the system considers energy consumption, traffic mobility, and driving comfort;
2. Unlike operating in a fixed-timing traffic light environment, the system can be dedicated to a DRL-based ATSC environment, while the proposed state sharing mechanism allows the ego vehicle to take the state of the traffic light agent as part of the state itself;3. Whereas conventional optimization-based methods suffer from computation complexity, a model-free DRL algorithm is adopted to generate the speed profile of the vehicle in real-time, and the SHAP approach is used to explain and visualize the decision of the ego vehicle, which is of help to reveal the black-box decision process of the DRL agent. The remainder of this article is structured as follows. Section 2 introduces the preliminaries of DRL and DRL-based ATSC. Section \"Problem formulation\" provides the formulation of the eco-driving control problem with ATSC. Section 4 proposes the MDP framework and PPO algorithm with the intent to solve the problem. Section 5 reports a series of simulations carried out in the SUMO platform [40] to demonstrate the feasibility and effectiveness of the proposed method. Finally, some concluding remarks are presented in Section 6.