Optimization-Based Hierarchical Motion Planning for Autonomous Racing

In this work 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.
The presented paper can be downloaded here: arxiv.org/abs/...
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[@fredericsidler]

I love your work. Amazing. Congrats.

[@ayiteavinash3856]

Great work guys

[@rezaenursaif6642]

You guys used anti-ackerman geometry

[@user-vh8bt5tq4n]

굳

[@baiyu8646]

Do you have the code for the lap time optimization?

[@anshumanparhi9378]

Hi team.. kindly give the link of the paper in the description.

[@AMZFormulaStudent]

Here is the link with the paper: arxiv.org/abs/2003.04882