Optimization-Based Hierarchical Motion Planning for Autonomous Racing

Understand

In this paper we propose a hierarchical controller for autonomous racing where the same vehicle model is used in a two level optimization framework for motion planning.

• The high-level controller computes a trajectory that minimizes the lap time, and the low-level nonlinear model predictive path following controller tracks the computed trajectory online.
• Following a computed optimal trajectory avoids online planning and enables fast computational times.
• The efficiency is further enhanced by the coupling of the two levels through a terminal constraint, computed in the high-level controller.