Planning trajectories for automated vehicles in urban environments requires methods with high generality, long planning horizons, and fast update rates. Using a path-velocity decomposition, we contribute a novel planning framework, which generates foresighted trajectories and can handle a wide variety of state and control constraints effectively. In contrast to related work, the proposed optimal control problems are formulated over space rather than time. This spatial formulation decouples environmental constraints from the optimization variables, which allows the application of simple, yet efficient shooting methods. To this end, we present a tailored solution strategy based on ILQR, in the Augmented Lagrangian framework, to rapidly minimize the trajectory objective costs, even under infeasible initial solutions. Evaluations in simulation and on a full-sized automated vehicle in real-world urban traffic show the real-time capability and versatility of the proposed approach.

翻译：在城市场景中为自动驾驶车辆规划轨迹需要具备高通用性、长规划周期和快速更新速率的方法。本文采用路径-速度分解方法，提出了一种新颖的规划框架，能够生成具有前瞻性的轨迹，并有效处理各类状态和约束条件。与现有工作不同，所提出的最优控制问题基于空间而非时间进行建模。这种空间公式化将环境约束与优化变量解耦，从而能够应用简单而高效的打靶法。为此，我们基于增广拉格朗日框架下的迭代线性二次调节器（ILQR），提出了一种定制化求解策略，即使在初始解不可行的情况下，也能快速最小化轨迹目标成本。仿真实验及在全尺寸自动驾驶车辆实际城市场景中的测试结果表明，该方法具有实时性与通用性。