Obstacle detection and platoon control for mixed traffic flows, comprising human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs), face challenges from uncertain disturbances, such as sensor faults, inaccurate driver operations, and mismatched model errors. Furthermore, misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’ perception and platoon safety. In this paper, we develop a two-dimensional robust control method for a mixed platoon, including a single leading CAV and multiple following HDVs that incorporate robust information sensing and platoon control. To effectively detect and locate unknown obstacles ahead of the leading CAV, we propose a cooperative vehicle–infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading CAV. This sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive manner. Additionally, we propose a distributed car-following control scheme with robustness to guarantee the following HDVs, considering uncertain disturbances. We also provide theoretical proof of the string stability under this control framework. Finally, extensive simulations are conducted to validate our approach. The simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers, significantly reduce the mean-square deviation in obstacle sensing, and approach the theoretical error lower bound. Moreover, the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.

中文翻译：

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用于避障的自动驾驶和人类驾驶混合车辆的鲁棒列队控制：一种基于协同感知的自适应模型预测控制方法


混合交通流（包括人类驾驶车辆 （HDV） 以及互联和自动驾驶汽车 （CAV））的障碍物检测和列队控制面临着来自不确定干扰的挑战，例如传感器故障、驾驶员操作不准确和模型不匹配。此外，车辆无线网络中的误导性传感信息或恶意攻击可能会危及 CAV 的感知和列队安全。在本文中，我们为混合排开发了一种二维鲁棒控制方法，包括一个领先的 CAV 和多个跟随的 HDV，它们结合了鲁棒信息传感和排控制。为了有效地检测和定位领先 CAV 前方的未知障碍物，我们提出了一种协作车辆-基础设施传感方案，并将其与领先 CAV 的自适应模型预测控制方案集成。这种传感方案融合了来自多个节点的信息，同时抑制了来自攻击者的恶意数据，以分布式和自适应的方式增强了稳健性和攻击弹性。此外，我们提出了一种具有鲁棒性的分布式跟车控制方案，以保证后续 HDV，考虑到不确定的干扰。我们还提供了该控制框架下字符串稳定性的理论证明。最后，进行了广泛的模拟以验证我们的方法。仿真结果表明，该方法可以有效地过滤掉恶意攻击者的误导性感知信息，显著降低障碍物感知的均方差，并接近理论误差下界。 此外，所提出的控制方法成功地实现了混合排的避障，同时确保了面对外部攻击和不确定干扰的稳定性和鲁棒性。