Autonomous vehicle (AV) technology is widely studied in both industrial and academic communities since it is regarded as a promising means for improving transportation safety and efficiency. Lane changing is a critical link for higher-level AV operations. However, few studies on AV lane changing consider the dynamics of surrounding vehicles, particularly in a mixed traffic environment including human-driven vehicles (HVs). Therefore, this article presents a dynamic lane-changing model for AV incorporating human driver behavior in mixed traffic. The proposed model includes four key components: car following (and lane keeping), lane-changing decision, dynamic trajectory generation, and model predictive control (MPC)-based trajectory tracking. AV longitudinal control algorithm is also depicted in detail in this article. Field experiments are conducted on a large-scale test track to test and validate the proposed model. An AV and three HVs are used in the lane-changing experiments. Different human driver behaviors are considered in the experiment settings. Experimental results show that the proposed lane-changing model can complete lane-changing maneuvers efficiently when HVs are cooperative and can also robustly abort them when HVs are uncooperative. Compared with the measured human lane-changing maneuvers, AV lane-changing maneuvers from the proposed model are more comfortable and safer.

中文翻译：

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与周围人类驾驶车辆自动换道的建模与现场实验

自动驾驶汽车 (AV) 技术被工业界和学术界广泛研究，因为它被认为是提高运输安全和效率的有前途的手段。变道是更高级别自动驾驶操作的关键环节。然而，很少有关于 AV 车道变换的研究考虑周围车辆的动态，特别是在包括人类驾驶车辆 (HV) 在内的混合交通环境中。因此，本文提出了一种包含混合交通中人类驾驶员行为的 AV 动态变道模型。所提出的模型包括四个关键组件：汽车跟随（和车道保持）、换道决策、动态轨迹生成和基于模型预测控制（MPC）的轨迹跟踪。AV纵向控制算法也在本文中详细描述。现场实验是在大规模测试轨道上进行的，以测试和验证所提出的模型。变道实验中使用了一个 AV 和三个 HV。在实验设置中考虑了不同的人类驾驶员行为。实验结果表明，所提出的换道模型可以在 HV 合作时有效地完成换道操作，并且在 HV 不合作时也可以稳健地中止它们。与实测的人类换道操作相比，所提出模型的 AV 换道操作更舒适、更安全。实验结果表明，所提出的换道模型可以在 HV 合作时有效地完成换道操作，并且在 HV 不合作时也可以稳健地中止它们。与实测的人类换道操作相比，所提出模型的 AV 换道操作更舒适、更安全。实验结果表明，所提出的换道模型可以在 HV 合作时有效地完成换道操作，并且在 HV 不合作时也可以稳健地中止它们。与实测的人类换道操作相比，所提出模型的 AV 换道操作更舒适、更安全。