Trajectory smoothing is an effective concept to control connected automated vehicles (CAVs) in mixed traffic to reduce traffic oscillations and improve overall traffic performance. However, smoother trajectories often lead to greater gaps between vehicles, which may incentivize human driven vehicles (HVs) from adjacent lanes to make cut-in lane changes. Such cut-in lane changes may compromise the expected performance from CAV trajectory smoothing. To figure out the reasons behind the issue, this paper designs a mixed traffic framework at a signalized intersection with multi-lane roads considering detailed trajectory control, car following and lane changing maneuvers all together. Based on the framework, this paper proposes a decentralized lane-change-aware CAV trajectory optimization model including discretionary lane change restraining and mandatory lane change yielding strategies. Riding comfort and traffic mobility are considered as a joint objective. And the complex non-linear lane-change-aware constraints are linearized to convert the proposed problem to a quadratic optimization problem. The linearization allows the investigated problem to be easily fed into a commercial solver. Numerical experiments are conducted to study the performance of the proposed model and to compare it with other models (e.g., a cooperative lane change model and a trajectory optimization model without the lane-change-aware mechanism) in different scenarios. First, results show that the HV lane changes cause reduction of half or more expected benefits of trajectory smoothing along a multi-lane segment adjacent to a signalized intersection. Then, we find that the proposed model outperforms the other models. Especially, the proposed model yields extra benefits in the system joint objective (10–25%), riding comfort (10–25%), travel time (1–8%), fuel consumption (3–15%) and safety (5–25%) compared with the trajectory optimization model without the lane-change-aware mechanism when CAV market penetration rate is not high. Sensitivity analyses on road segment lengths, signal cycle lengths, traffic saturation rates and through-vehicle rates show that the proposed model yields better system performance under most scenarios, e.g., 20% extra benefit at a short road segment length, 30% extra benefit at a long signal cycle length, 25% extra benefit at a high traffic saturation rate, and 25% extra benefit at a high through-vehicle rate.

轨迹平滑是在混合交通中控制联网自动驾驶汽车 (CAV) 以减少交通振荡并提高整体交通性能的有效概念。然而，更平滑的轨迹通常会导致车辆之间的差距更大，这可能会激励相邻车道的人类驾驶车辆 (HV) 进行切入车道变更。这种切入车道变化可能会影响 CAV 轨迹平滑的预期性能。为了找出问题背后的原因，本文在多车道道路的信号交叉口设计了混合交通框架，同时考虑了详细的轨迹控制、车辆跟随和换道操作。基于框架，本文提出了一种分散式换道感知 CAV 轨迹优化模型，包括自主换道约束和强制换道屈服策略。乘坐舒适性和交通机动性被视为共同目标。并且将复杂的非线性车道变化感知约束线性化以将所提出的问题转换为二次优化问题。线性化允许将所研究的问题轻松输入商业求解器。进行了数值实验以研究所提出模型的性能，并将其与其他模型（例如，协作变道模型和没有变道感知机制的轨迹优化模型）在不同场景下进行比较。第一的，结果表明，HV 车道变化导致沿与信号交叉口相邻的多车道段的轨迹平滑的预期收益减少一半或更多。然后，我们发现所提出的模型优于其他模型。特别是，所提出的模型在系统联合目标（10-25%）、乘坐舒适性（10-25%）、行驶时间（1-8%）、油耗（3-15%）和安全性（5 –25%) 与 CAV 市场渗透率不高时没有车道变化感知机制的轨迹优化模型相比。对路段长度、信号周期长度、交通饱和率和通过车辆率的敏感性分析表明，所提出的模型在大多数情况下产生了更好的系统性能，例如，在较短的路段长度下，20% 的额外收益，