Applications of Successive Convexification in Autonomous VehiclePlanning and Control
Abstract
In this paper, we present the application of successive convexification methods to autonomous driving problems borrowed from recent aerospace literature. We formulate two optimization problems within the successive convexification framework. Using arc-length parametrization in the vehicle kinematic model, we solve the speed planning and model predictive control problems with a range of constraints and obstacle configurations. This paper is the first systematic application of successive convexification methods from the aerospace literature to the autonomous driving problems. In addition, we show a simple application of logical state-trigger constraints in a continuous formulation of the optimization by including an evasion maneuver in the simulations section. We give details of the problem formulation and implementation and present and discuss the results.
Keywords
Cite
@article{arxiv.2010.06276,
title = {Applications of Successive Convexification in Autonomous VehiclePlanning and Control},
author = {Ali Boyali and Simon Thompson and David Wong},
journal= {arXiv preprint arXiv:2010.06276},
year = {2020}
}
Comments
Presented at ICACR 2020