Trajectory planning in automated driving typically focuses on satisfying safety and comfort requirements within the vehicle's onboard sensor range. This paper introduces a method that leverages anticipatory road data, such as speed limits, road slopes, and traffic lights, beyond the local perception range to optimize energy-efficient braking trajectories. For that, coasting, which reduces energy consumption, and active braking are combined to transition from the current vehicle velocity to a lower target velocity at a given distance ahead. Finding the switching instants between the coasting phases and the continuous control for the braking phase is addressed as an optimal trade-off between maximizing coasting periods and minimizing braking effort. The resulting switched optimal control problem is solved by deriving necessary optimality conditions. To facilitate the incorporation of additional feasibility constraints for multi-phase trajectories, a sub-optimal alternative solution based on parametric optimization is proposed. Both methods are compared in simulation.

翻译：自动驾驶中的轨迹规划通常侧重于满足车辆车载传感器范围内的安全性与舒适性要求。本文提出一种方法，利用超出局部感知范围的前瞻性道路数据（如限速、道路坡度和交通信号灯）来优化节能制动轨迹。为此，将降低能耗的滑行与主动制动相结合，使车辆在给定前方距离内从当前速度过渡至较低的目标速度。寻找滑行阶段与制动阶段连续控制之间的切换时刻，被处理为最大化滑行时间与最小化制动能耗之间的最优权衡问题。通过推导必要的最优性条件，求解由此产生的切换最优控制问题。为便于纳入多阶段轨迹的附加可行性约束，提出了一种基于参数优化的次优替代解决方案。两种方法在仿真中进行了对比。