@inproceedings{d6555cdb03f640be97479a91eaa59fcf,
title = "Proximate model predictive control strategy for autonomous vehicle lateral control",
abstract = "A proximate model predictive control strategy is proposed for autonomous vehicle lateral control, which substantially reduces iteration in on-line optimization. Nodal state vectors are generated in the feasible state space, for which the quadratic optimization problem is solved off-line. Vertices are determined to represent a given state as an interpolation between them. An approximate optimal solution is computed from the interpolation between the optimal solutions at each vertex, and is used for warm-start on-line optimization to produce a proximate optimal solution. The proposed proximate model prediction control is shown to exhibit proximate optimality in very few on-line iterations.",
keywords = "Autonomous vehicles, Model predictive control, Proximate optimality, Quadratic programming",
author = "Lee, {Seung Hi} and Lee, {Young Ok} and Youngseop Son and Chung, {Chung Choo}",
year = "2011",
language = "English",
isbn = "9781457708350",
series = "International Conference on Control, Automation and Systems",
pages = "590--595",
booktitle = "ICCAS 2011 - 2011 11th International Conference on Control, Automation and Systems",
note = "2011 11th International Conference on Control, Automation and Systems, ICCAS 2011 ; Conference date: 26-10-2011 Through 29-10-2011",
}