This paper proposes a multi-vehicle cooperative control scheme in mitigating traffic oscillation (MCCS-MTO) for a single-vehicle lane change (LC) scenario applied to connected and autonomous vehicles (CAVs) with guaranteed executing applicability. Specifically, a hierarchical structure is applied in the proposed MCCS-MTO to dampen traffic oscillation on both original and target lanes. It decomposes the MCCS-MTO into two subsets of controllers (i.e. upper-layer and low-layer). The upper-layer controller first regards the LC vehicle and the following vehicle on the target lane as control objects and optimally controls their movements by considering the ambient traffic conditions. The lower-layer controller consecutively controls the moving status of the following vehicle on the original lane according to the LC vehicle’s optimized predictive state outputs obtained from the upper-layer and the surrounding traffic. The vehicle dynamic is modeled by incorporating the lateral and longitudinal movements of the LC vehicle into unified control quantities (i.e. axial acceleration and steering angle) to enhance the executing applicability. Both upper-layer and low-layer controllers in the proposed MCCS-MTO are established by leveraging the model predictive control and considering the longitudinal tracking performance, transient traffic smoothness, and lateral LC efficiency with multiple safety constraints. To guarantee a smooth and collision-free tracking performance when preceding vehicles switch, a set of innovative negative exponential and S-shaped functions is designed and integrated into both constraints and objective functions. To ensure the successful implementation of the proposed method, the sequential feasibility properties for the proposed MCCS-MTO are theoretically proven. The proposed approach is validated through numerical experiments in Python with multiple LC scenarios from the NGSIM dataset. The results of the numerical experiments indicate that the proposed study can execute a safe and efficient LC while performing a smooth longitudinal movement that will improve traffic efficiency.

本文针对单车变道（LC）场景提出了一种减轻交通振荡的多车协同控制方案（MCCS-MTO），应用于联网自动驾驶车辆（CAV），并保证执行适用性。具体来说，在所提出的 MCCS-MTO 中应用了分层结构，以抑制原始车道和目标车道上的交通振荡。它将MCCS-MTO分解为两个控制器子集（即上层和低层）。上层控制器首先将目标车道上的LC车辆和跟随车辆作为控制对象，并结合周围的交通状况来优化控制它们的运动。下层控制器根据上层和周围交通获得的LC车辆的优化预测状态输出，连续控制原车道上后续车辆的移动状态。通过将LC车辆的横向和纵向运动纳入统一的控制量（即轴向加速度和转向角）来对车辆动力学进行建模，以增强执行适用性。所提出的MCCS-MTO中的上层和低层控制器都是通过利用模型预测控制并考虑纵向跟踪性能、瞬态交通平滑性和具有多重安全约束的横向LC效率来建立的。为了保证前车切换时平稳、无碰撞的跟踪性能，设计了一组创新的负指数函数和 S 形函数，并将其集成到约束和目标函数中。为了确保所提出方法的成功实施，从理论上证明了所提出的 MCCS-MTO 的顺序可行性特性。所提出的方法通过 Python 中的数值实验以及来自 NGSIM 数据集的多个 LC 场景进行了验证。数值实验的结果表明，所提出的研究可以执行安全高效的LC，同时执行平滑的纵向移动，从而提高交通效率。