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arXiv · 2003.04882

Optimization-Based Hierarchical Motion Planning for Autonomous Racing

José L. Vázquez·Marius Brühlmeier·Alexander Liniger·Alisa Rupenyan·John Lygeros
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Abstract

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. Including this constraint in the real-time optimization level ensures that the prediction horizon can be shortened, while safety is guaranteed. This proves crucial for the experimental validation of the approach on a full size driverless race car. The vehicle in question won two international student racing competitions using the proposed framework; moreover, our hierarchical controller achieved an improvement of 20% in the lap time compared to the state of the art result achieved using a very similar car and track.

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José L. Vázquez, Marius Brühlmeier, Alexander Liniger, Alisa Rupenyan, John Lygeros. 2020-03-10. Optimization-Based Hierarchical Motion Planning for Autonomous Racing. https://arxiv.org/abs/2003.04882

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