Motion planning in dynamically changing environments is one of the most complex challenges in autonomous driving. Safety is a crucial requirement, along with driving comfort and speed limits. While classical sampling-based, lattice-based, and optimization-based planning methods can generate smooth and short paths, they often do not consider the dynamics of the environment. Some techniques do consider it, but they rely on updating the environment on-the-go rather than explicitly accounting for the dynamics, which is not suitable for self-driving. To address this, we propose a novel method based on the Neural Field Optimal Motion Planner (NFOMP), which outperforms state-of-the-art approaches in terms of normalized curvature and the number of cusps. Our approach embeds previously known moving obstacles into the neural field collision model to account for the dynamics of the environment. We also introduce time profiling of the trajectory and non-linear velocity constraints by adding Lagrange multipliers to the trajectory loss function. We applied our method to solve the optimal motion planning problem in an urban environment using the BeamNG.tech driving simulator. An autonomous car drove the generated trajectories in three city scenarios while sharing the road with the obstacle vehicle. Our evaluation shows that the maximum acceleration the passenger can experience instantly is -7.5 m/s^2 and that 89.6% of the driving time is devoted to normal driving with accelerations below 3.5 m/s^2. The driving style is characterized by 46.0% and 31.4% of the driving time being devoted to the light rail transit style and the moderate driving style, respectively.

翻译：在动态变化环境中的运动规划是自动驾驶中最复杂的挑战之一。安全性是与驾驶舒适性和速度限制同等重要的关键要求。尽管经典的基于采样、基于栅格和基于优化的规划方法能够生成平滑且路径较短的轨迹，但它们通常未考虑环境的动态性。部分技术考虑了这一因素，却依赖于实时更新环境而非显式处理动态性，这并不适用于自动驾驶。为此，我们提出了一种基于神经场最优运动规划器（NFOMP）的新方法，该方法在归一化曲率和拐点数量方面优于现有最优技术。本方法将已知运动障碍物嵌入神经场碰撞模型中，以显式考虑环境动态性。此外，我们通过向轨迹损失函数添加拉格朗日乘子，引入了轨迹的时间剖面分析以及非线性速度约束。我们采用BeamNG.tech驾驶仿真器将该方法应用于城市环境中的最优运动规划问题。在三个城市场景中，自动驾驶汽车与被控障碍车辆共享道路时沿生成的轨迹行驶。评估结果显示，乘客能瞬时承受的最大加速度为-7.5 m/s²，且89.6%的驾驶时间加速度低于3.5 m/s²，属于正常驾驶模式。从驾驶风格来看，46.0%和31.4%的驾驶时间分别对应轻轨交通风格和适中驾驶风格。