Real-Time Spatial Trajectory Planning Under Lateral Constraints

Trajectory planning is an essential component in automated driving applications. Especially planning in urban environments imposes a multitude of constraints, which need to be handled within a short computational time frame. Towards this challenge, we propose a novel trajectory planning approach based on a spatial problem formulation, which can update at high rates over long horizons. Complementing our prior work on longitudinal optimization, this approach focuses on planning under lateral constraints. Using a constraint shaping step followed by an optimal control solution in a Frenet frame, a comfortable and anticipatory trajectory is generated. For solving the resulting dynamic optimization objectives, a tailored solution strategy based on the iterative linear quadratic regulator (ILQR) in the Augmented-Lagrangian framework is employed. Simulated results on various urban scenarios show the effectiveness and versatility of the approach.