## 6 Conclusions  
In this paper, we propose a reinforcement learning framework to implement eco-driving control for electric vehicles in the ATSC environment. The eco-driving problem is converted to an MDP because it is difficult to solve the optimal control problem directly.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\omega_{1}\) & \(\omega_{2}\) & \(\omega_{3}\) & **Energy Consumption (Wh)** & **Travel Time (s)** & **Mean Jerk (m/s\({}^{3}\))** \\ \hline
1 & 1 & 4 & 77.47 & 650.10 & 0.54 \\
1 & 1 & 7 & 72.32 & 644.10 & 0.56 \\
1 & 1 & 10 & 80.59 & 567.30 & 0.47 \\
1 & 2 & 4 & 72.56 & 62  
The MDP has been carefully designed: the state of the vehicle includes not only its dynamic parameters but also the shared state of the signal agents, while the state sharing mechanism can help the ego vehicle make decisions in an uncertain environment. The reward function considers mobility, energy consumption, and comfort to enhance the driving strategy. Besides, an explanation module is established to explain the decision process of the ego vehicle, which can make the black-box model-free method clear and intuitive. The simulations are carried out on a signalized corridor built in SUMO. Being compared with RAINBOW and IDM, the proposed PPO algorithm gives a much better energy-saving performance. Despite the traffic mobility being reduced, this method can be used in circumstances where the requirement of travel time is not high, or energy is extremely scarce. Moreover, the SHAP value explicates the decision basis of the ego vehicle well, and this can provide reliability and safety for the actual circumstances.  
It should be pointed out that the proposed strategy Is still feasible for gasoline vehicles. Although we implement a RAINBOW-based ATSC, the whole framework can be applied to the ATSC environments controlled by some other algorithms.  
This paper only considered an ideal V2I communication situation, but packet loss and communication delay are common in real world, so it is practical to study these conditions in future searches. Meanwhile, we only put forward the strategy for a single vehicle, while the corresponding methodology can be extended to platoon control, and this may be implemented in future research. Another research gap is that the interaction between vehicles with eco-driving systems and normal human-driven vehicles is not clear. Since the operation of eco-driving vehicles may have a direct impact on the holistic traffic flow, it is of value to explore the topic in a dedicated study. In addition, some data can be collected from a real-world vehicle operation system and used for training agents in an offline manner with offline DRL technique, which is helpful to formulate more applicable strategies to surmount the simulation-to-real transfer gap.  
Conceptualization, [PERSON] and J.Z.; methodology, [PERSON]; software, [PERSON] and B.W.; validation, [PERSON], [PERSON] and B.W.; formal analysis, [PERSON]; investigation, X.J. and B.W.; data curation, [PERSON] and B.W.; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key R&D Program of China (Grant 2021 TFB-1600500), and the Key R&D Program of Jiangsu Province in China (Grant No. BE2020013). The work of the first author is supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. SJCX21_0062).  
Not applicable.  
Data Availability Statement: Data in this study were produced through simulation experiments. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.