  The urban environment is amongst the most difficult domains for autonomous
vehicles. The vehicle must be able to plan a safe route on challenging road
layouts, in the presence of various dynamic traffic participants such as
vehicles, cyclists and pedestrians and in various environmental conditions. The
challenge remains to have motion planners that are computationally fast and
that account for future movements of other road users proactively. This paper
describes an computationally efficient sampling-based trajectory planner for
safe and comfortable driving in urban environments. The planner improves the
Stable-Sparse-RRT algorithm by adding initial exploration branches to the
search tree based on road layout information and reiterating the previous
solution. Furthermore, the trajectory planner accounts for the predicted motion
of other traffic participants to allow for safe driving in urban traffic.
Simulation studies show that the planner is capable of planning collision-free,
comfortable trajectories in several urban traffic scenarios. Adding the
domain-knowledge-based exploration branches increases the efficiency of
exploration of highly interesting areas, thereby increasing the overall
planning performance.
