基于基础设施的弯道受限互联自动车辆轨迹优化：一种基于曲率坐标的空间表达方法
Infrastructure Assisted Constrained Connected Automated Vehicle Trajectory Optimization on Curved Roads: A Spatial Formulation on a Curvilinear Coordinate
摘要：
车辆轨迹优化是保证车辆高效、安全行驶的关键。提出了一种基于基础设施辅助的有约束自动驾驶汽车弯道轨迹优化方法。本文系统地阐述了基于曲线坐标的问题，这是灵活的模型复杂的道路几何形状。此外，为了处理空间变化的道路障碍物，交通规则和几何特征，二维车辆运动学给出了一个空间公式与基础设施提供的准确的道路信息。因此，我们采用了多目标模型预测控制（MPC）的方法来优化的轨迹在滚动的地平线，同时满足碰撞避免和车辆的运动学约束。为了验证我们的方法的有效性，进行了数值模拟。结果表明，该方法可以提供平滑的车辆轨迹，避免道路障碍物，并同时遵循交通规则，这是鲁棒的道路几何形状和干扰。
英文摘要：
Vehicle trajectory optimization is essential to ensure vehicles travel efficiently and safely. This paper presents an infrastructure assisted constrained connected automated vehicles (CAVs) trajectory optimization method on curved roads. This paper systematically formulates the problem based on a curvilinear coordinate which is flexible to model complex road geometries. Further, to deal with the spatial varying road obstacles, traffic regulations, and geometric characteristics, two-dimensional vehicle kinematics is given in a spatial formulation with exact road information provided by the infrastructure. Consequently, we applied a multi-objective model predictive control (MPC) approach to optimize the trajectories in a rolling horizon while satisfying the collision avoidances and vehicle kinematics constraints. To verify the efficiency of our method, a numerical simulation is conducted. As the results suggest, the proposed method can provide smooth vehicular trajectories, avoid road obstacles, and simultaneously follow traffic regulations, which is robust to road geometries and disturbances.
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