Lane-change-aware connected automated vehicle trajectory optimization at a signalized intersection with multi-lane roads
Lane-change-aware connected automated vehicle trajectory optimization at a signalized intersection with multi-lane roads
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DOI：
10.1016/j.trc.2021.103182
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发表时间：
2021-06-04
影响因子：
8.3
通讯作者：
Li, Xiaopeng
中科院分区：
文献类型：
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作者：
Yao, Handong;Li, Xiaopeng
轨迹平滑是一种有效的概念，以控制连接自动车辆（CAV）在混合交通，以减少交通振荡，提高整体交通性能。然而，更平滑的轨迹通常导致车辆之间的更大间隙，这可能激励来自相邻车道的人类驾驶车辆（HV）进行切入车道改变。这种切入车道变化可能会损害CAV轨迹平滑的预期性能。为了找出问题背后的原因，本文设计了一个混合交通框架，在信号交叉口与多车道的道路，考虑详细的轨迹控制，跟驰和换道机动一起。在此基础上，提出了一种分散式变道感知的CAV轨迹优化模型，该模型包括任意变道约束和强制变道让步策略。乘坐舒适性和交通流动性被认为是一个共同的目标。对复杂的非线性变道约束进行线性化处理，将问题转化为二次优化问题。线性化允许调查的问题，很容易被送入一个商业求解器。通过数值实验研究了该模型的性能，并与其他模型（例如，合作车道变换模型和没有车道变换感知机制的轨迹优化模型）。首先，结果表明，HV车道变化导致减少一半或更多的预期效益的轨迹平滑沿着相邻的信号交叉口的多车道段。然后，我们发现，该模型优于其他模型。特别是，在CAV市场渗透率不高的情况下，与不带换道感知机制的轨迹优化模型相比，该模型在系统联合目标（10 - 25%）、乘坐舒适性（10-25%）、行程时间（1-8%）、燃油消耗（3-15%）和安全性（5-25%）方面都有额外的收益。对路段长度、信号周期长度、交通饱和率和车辆通过率的敏感性分析表明，在大多数情况下，在短路段长度下的20%额外优惠，在长信号周期长度下的30%额外优惠，在高交通饱和率下的25%额外优惠，以及在高车辆通过率下的25%额外优惠。
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.