Autonomous driving vehicles aim to free the hands of vehicle operators, helping them to drive easier and faster, meanwhile, improving the safety of driving on the highway or in complex scenarios. Automated driving systems (ADS) are developed and designed in the last several decades to realize fully autonomous driving vehicles (L4 or L5 level). The scale of sampling space leads to the main computational complexity. Therefore, by adjusting the sampling method, the difficulty to solve the real-time motion planning problem could be incrementally reduced. Usually, the Average Sampling Method is taken in Lattice Planner, and Random Sampling Method is chosen for RRT algorithms. However, both of them don't take into consideration the prior information, and focus the sampling space on areas where the optimal trajectory is previously obtained. Therefore, \emph{in this thesis it is proposed an adaptive sampling method to reduce the computation complexity, and achieve faster solutions while keeping the quality of optimal solution unchanged}. The main contribution of this thesis is the significant decrease in the complexity of the optimization problem for motion planning, without sacrificing the quality of the final trajectory output, with the implementation of an Adaptive Sampling method based on Artificial Potential Field (ASAPF). In addition, also the quality and the stability of the trajectory is improved due to the appropriate sampling of the appropriate region to be analyzed.

翻译：自动驾驶车辆旨在解放车辆操作者的双手，帮助其更轻松、快捷地驾驶，同时提升高速公路或复杂场景下的行车安全性。近几十年来，为完全实现全自动驾驶车辆（L4或L5级别），研发并设计了自动驾驶系统（ADS）。采样空间的规模导致了主要的计算复杂度。因此，通过调整采样方法，可以逐步降低实时运动规划问题的求解难度。通常，在Lattice Planner中采用平均采样方法，而在RRT算法中则选用随机采样方法。然而，这两种方法均未考虑先验信息，也未将采样空间聚焦于先前已获得最优轨迹的区域。因此，本文提出了一种自适应采样方法，旨在降低计算复杂度，在保持最优解质量的同时实现更快的求解速度。本文的主要贡献在于，通过实施基于人工势场的自适应采样方法（ASAPF），在显著降低运动规划优化问题复杂度的同时，不牺牲最终轨迹输出的质量。此外，由于对需分析区域的精准采样，轨迹的质量与稳定性也得到了提升。