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The urban environment is amongst the most difficult domains for autonomousvehicles. The vehicle must be able to plan a safe route on challenging roadlayouts, in the presence of various dynamic traffic participants such asvehicles, cyclists and pedestrians and in various environmental conditions. Thechallenge remains to have motion planners that are computationally fast andthat account for future movements of other road users proactively. This paperdescribes an computationally efficient sampling-based trajectory planner forsafe and comfortable driving in urban environments. The planner improves theStable-Sparse-RRT algorithm by adding initial exploration branches to thesearch tree based on road layout information and reiterating the previoussolution. Furthermore, the trajectory planner accounts for the predicted motionof 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 thedomain-knowledge-based exploration branches increases the efficiency ofexploration of highly interesting areas, thereby increasing the overallplanning performance.

Informed sampling-based trajectory planner for automated driving in dynamic urban environments