To optimize the mixed car-following behavior of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs), an adaptive backstepping sliding mode control (ABSMC) strategy for longitudinal velocity and distance control model (namely, MCF-ABSMCM) is put forth. First, the variable desired headway model (VDHM) is designed based on the vehicle-to-vehicle and vehicle-to-infrastructure information. Also, an asymmetric stochastic car-following model (ASCM) is designed to realistically describe the stochastic factor as well as vehicle acceleration and braking behaviors in mixed car-following. Second, an adaptive cruise sliding mode control law with a backstepping approach is devised to precisely track the desired following distance based on the VDHM. After creating the basic diagram of the two-model traffic flow, the intelligent driver model (IDM) is used as a baseline to compare and analyze the traffic capacity. Lastly, the effectiveness of the MCF-ABSMCM approach is confirmed in terms of both the mixed traffic flow density wave evolution and the vehicle control strategy, and the MCF-ABSMCM is validated under various adversary inputs. The simulation results demonstrate that the mixed-flow queue can finish headway tracking and keep a safe distance when using the MCF-ABSMCM described. This control strategy has been validated in VISSIM to significantly improve the efficiency of traffic operations compared to IDM and ASCM.

中文翻译：

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基于动态期望车头时距的自适应反步滑模控制设计在混合交通系统中的应用


为了优化联网自动驾驶车辆（CAV）和人类驾驶车辆（HDV）的混合跟车行为，提出了纵向速度和距离控制模型的自适应反步滑模控制（ABSMC）策略（即MCF-ABSMCM）向前。首先，基于车辆与车辆和车辆与基础设施信息设计可变期望车头时距模型（VDHM）。此外，还设计了非对称随机跟驰模型（ASCM）来真实地描述混合跟驰中的随机因素以及车辆加速和制动行为。其次，设计了一种采用反步法的自适应巡航滑模控制律，以基于 VDHM 精确跟踪所需的跟随距离。创建两种模型交通流的基本图后，以智能驾驶员模型（IDM）为基线来比较和分析交通容量。最后，MCF-ABSMCM 方法的有效性在混合交通流密度波演化和车辆控制策略方面得到了证实，并且 MCF-ABSMCM 在各种对手输入下得到了验证。仿真结果表明，使用所描述的MCF-ABSMCM时，混流队列可以完成车头跟踪并保持安全距离。该控制策略已在 VISSIM 中得到验证，与 IDM 和 ASCM 相比，可显着提高交通运营效率。