Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles (AVs). The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer. To address this issue, this study proposes a safe motion planning and control (SMPAC) framework for AVs. For the control layer, a dynamic model including multi-dimensional uncertainties is established. A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set. A flexible tube with varying cross-sections is constructed to reduce the controller conservatism. For the planning layer, a concept of safety sets, representing the geometric boundaries of the ego vehicle and obstacles under uncertainties, is proposed. The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories. An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles. A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC. The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.

中文翻译：

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采用 Zonotopic TRMPC 的自动驾驶车辆安全运动规划和控制框架

模型不匹配可能会导致自动车辆（AV）后退控制策略的多维不确定性。当 AV 跟踪由 AV 规划层单独优化的理想轨迹时，不确定性可能会导致潜在的危险行为。为了解决这个问题，本研究提出了自动驾驶汽车的安全运动规划和控制（SMPAC）框架。对于控制层，建立了包含多维不确定性的动态模型。提出了一种基于区域管的鲁棒模型预测控制方案，将不确定系统约束在有界最小鲁棒正不变集中。构造具有不同横截面的柔性管以减少控制器的保守性。对于规划层，提出了安全集的概念，代表不确定性下自我车辆和障碍物的几何边界。安全集为生成的轨迹的后续评估和排序提供了基础。有效的防撞算法通过自我车辆和障碍物之间安全集的交叉检测来决定所需的轨迹。数值模拟和硬件在环实验验证了 SMPAC 的有效性和实时性能。两个驾驶场景的结果表明，SMPAC能够保证多维不确定性下自动驾驶的安全。