## 1 Introduction  
Urban intersections often suffer from severe congestion, yet they play a crucial role in allocating conflicting streams of traffic. Traditional intersection management relies on static signal traffic lights coordinated through timers [1]. However, this strategy, which is solely based on predefined time cycles, overlooks variations in actual vehicle density and travel velocities on the road, potentially leading to ineffective responses to diverse traffic demands [2]. With the rapid advancement of electric and autonomous vehicles, Connected and Automated Vehicles (CAVs) hold promise for revolutionary changes in traffic networks, substantially enhancing network performance [3, 4]. By harnessing Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, CAVs can access real-time travel data from adjacent vehicles and interact with infrastructure, thus enabling the possibility of signal-free intersections [5, 6]. Effectively and securely coordinating CAVs through signal-free intersections necessitates the formulation of diverse cooperative driving strategies. These strategies typically optimize one or more objectives by orchestrating the crossing sequence and travel trajectories of all CAVs, thereby addressing the Crossing Sequence Problem (CSP) and the Trajectory Optimization Problem (TOP) for CAVs. Certain studies have deemed the First-In-First-Out (FIFO) strategy as a feasible approach to cooperative driving. Under this paradigm, the resolution of the CSP directly hinges upon the sequence of entry of each CAV into the intersection zone [7, 8]. Relative to the conventional traffic light strategy, the FIFO strategy exhibits noteworthy reductions in travel delay, thereby emerging as a robust mechanism for mitigating traffic congestion. Researchers have developed a decentralized energy-optimal control framework to precisely compute the controllable arrival times and optimal control inputs for each CAV, thereby offering a solution to the TOP [9]. Augmenting this framework with safety distance constraints and considerations for passenger comfort can augment the robustness of strategies [10]. Nonetheless, contemporary research underscores a principal constraint of the FIFO strategy, particularly its incapacity to dynamically reconfigure the sequencing of CAVs' crossings [11]. This limitation is acutely evident in scenarios of heightened traffic demands, curtailing further enhancements in intersection capacity and impeding the maximal utilization of CAV capabilities in addressing traffic congestion. Consequently, in the pursuit of more sophisticated cooperative driving strategies, prioritizing investigations into the CSP is imperative. The resequencing strategy aims to optimize the crossing sequence of CAVs. This approach employs a Resequencing Algorithm (RA) to address the CSP, generating multiple feasible crossing sequences for each CAV and selecting the optimal sequence. Throughout this process, maintaining the relative crossing sequence among CAVs is crucial while accommodating the insertion of newly entering CAVs [12]. However, this strategy is constrained to adjusting the crossing sequence of the most recently entered CAV, resulting in suboptimal sorting outcomes. To enhance the effectiveness of sorting results, the Dynamic Resequencing (DR) strategy proposes a comprehensive and independent re-evaluation of the crossing sequences of all CAVs each time a new CAV enters the intersection zone [13]. Although this approach may enhance sorting accuracy, it also significantly prolongs the runtime of the RA. In response to this challenge, the Local Dynamic Resequencing (LDR) strategy has been introduced, effectively reducing the number of CAVs requiring crossing sequence adjustments and thus shortening the runtime of the RA [14]. However, it is important to note that these strategies are predominantly event-triggered, leading to frequent updates of the optimal crossing sequence derived from the RA.