Informed sampling-based trajectory planner for automated driving in dynamic urban environments
van der Ploeg, C.
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.
To reference this document use:
Intelligent vehicle highway systems
Knowledge based systems
Roads and streets
Dynamic urban environments
Institute of Electrical and Electronics Engineers Inc.
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022, 8 October 2022 through 12 October 2022, 1690-1697