Real-Time Spatial Trajectory Planning for Urban Environments Using Dynamic Optimization
Abstract
Planning trajectories for automated vehicles in urban environments requires methods with high generality, long planning horizons, and fast update rates. Using a path-velocity decomposition, we contribute a novel planning framework, which generates foresighted trajectories and can handle a wide variety of state and control constraints effectively. In contrast to related work, the proposed optimal control problems are formulated over space rather than time. This spatial formulation decouples environmental constraints from the optimization variables, which allows the application of simple, yet efficient shooting methods. To this end, we present a tailored solution strategy based on ILQR, in the Augmented Lagrangian framework, to rapidly minimize the trajectory objective costs, even under infeasible initial solutions. Evaluations in simulation and on a full-sized automated vehicle in real-world urban traffic show the real-time capability and versatility of the proposed approach.
Cite
@article{arxiv.2305.02621,
title = {Real-Time Spatial Trajectory Planning for Urban Environments Using Dynamic Optimization},
author = {Jona Ruof and Max Bastian Mertens and Michael Buchholz and Klaus Dietmayer},
journal= {arXiv preprint arXiv:2305.02621},
year = {2023}
}