## 6 Conclusions  
This research established a centralized model for the CSP and TOP concerning CAVs. An advanced RA approach grounded on state transition networks was proposed and fused with a PFA to formulate the RPA. The objective was to achieve the global optimal solution of the centralized CSP within a finite time. Additionally, an enhanced controllable arrival time recursive algorithm tailored for CAV platoons was developed to impose time constraints on the energy-optimal control problem, effectively tackling the TOP. Subsequently, these novel algorithms were integrated to introduce an innovative strategy--the DRP strategy. To validate the efficacy of this strategy, we conducted integrated simulations of symmetrical intersections across diverse traffic demands using Python 3.10.7, Traci 1.19.0, and SUMO 1.19.0. The performance and fairness of CAVs under diverse driving strategies were evaluated. Findings indicated that the DRP strategy outperformed others significantly in terms of driving performance, exhibiting reduced travel delay and fuel consumption. However, concerning fairness, the DRP strategy showed a marginally inferior performance. This was ascribed to the influence of frequent adjustments in CAV passing sequences on fairness. Nonetheless, experiments also suggest that, in situations of heightened traffic  
Figure 11: The trip duration of CAVs under different driving strategies at arrival rates of 800 veh/h/lane.  
demand, strategies endowed with augmented traffic coordination capabilities can alleviate traffic congestion while concurrently upholding fairness from an alternative standpoint. Furthermore, it was noted that cooperative driving strategies were more proficient in enhancing energy efficiency compared to the traffic light strategy. This was chiefly attributed to the mitigation of intense acceleration and deceleration during start-stop scenarios. This observation offers valuable insights for further enhancing the energy efficiency of CAVs.  
The primary limitation of this study lies in its focus on the analysis of single-lane, one-way symmetric intersection scenarios. However, the conclusions drawn have the potential to be extended to multi-lane intersections and scenarios involving turning maneuvers by CAVs. Additionally, earlier discussions have revealed that there is still room for improvement in terms of fairness with regard to the DRP strategy. Subsequent research endeavors could explore strategies for striking a balance between driving performance and fairness within the DRP strategy and devise mathematical frameworks to quantitatively assess the equilibrium between these factors. Such endeavors would serve to augment drivers' contentment with traffic control systems and intersection layouts, thus fostering progress in the realm of intelligent transportation systems.  
Conceptualization, J.G.; methodology, J.G. and J.T.; software, J.G.; validation, J.G.; formal analysis, J.G. and J.T.; investigation, J.G.; resources, J.G. and L.G.; data curation, [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON], [PERSON], [PERSON], and [PERSON]; visualization, [PERSON] and [PERSON]; supervision, [PERSON] and [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research received partial support from the Natural Science Foundation of Shanghai (Grant No. 17 ZR1411900) and the Industry-University-Research Innovation Funds for Chinese Universities (Grant No. 2021 ZYB01003).  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.