Motion trajectory planning is one crucial aspect for automated vehicles, as it governs the own future behavior in a dynamically changing environment. A good utilization of a vehicle's characteristics requires the consideration of the nonlinear system dynamics within the optimization problem to be solved. In particular, real-time feasibility is essential for automated driving, in order to account for the fast changing surrounding, e.g. for moving objects. The key contributions of this paper are the presentation of a fast optimization algorithm for trajectory planning including the nonlinear system model. Further, a new concurrent operation scheme for two optimization algorithms is derived and investigated. The proposed algorithm operates in the submillisecond range on a standard PC. As an exemplary scenario, the task of driving along a challenging reference course is demonstrated.
@article{arxiv.1807.11039,
title = {Fast Trajectory Planning for Automated Vehicles using Gradient-based Nonlinear Model Predictive Control},
author = {Franz Gritschneder and Knut Graichen and Klaus Dietmayer},
journal= {arXiv preprint arXiv:1807.11039},
year = {2018}
}