  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.
