@inproceedings{ffa4484488084b71a07473730c0d95ae,
title = "Search-Based Motion Planning for Performance Autonomous Driving",
abstract = "Driving on the limits of vehicle dynamics requires predictive planning of future vehicle states. In this work, a search-based motion planning is used to generate suitable reference trajectories of dynamic vehicle states with the goal to achieve the minimum lap time on slippery roads. The search-based approach enables to explicitly consider a nonlinear vehicle dynamics model as well as constraints on states and inputs so that even challenging scenarios can be achieved in a safe and optimal way. The algorithm performance is evaluated in simulated driving on a track with segments of different curvatures. Our code is available at https://git.io/JenvB.",
keywords = "Autonomous vehicles, Drifting, Motion planning, Trail-braking",
author = "Zlatan Ajanovic and Enrico Regolin and Georg Stettinger and Martin Horn and Antonella Ferrara",
year = "2020",
month = jan,
day = "1",
doi = "10.1007/978-3-030-38077-9_134",
language = "English",
isbn = "9783030380762",
series = "Lecture Notes in Mechanical Engineering",
publisher = "Springer",
pages = "1144--1154",
editor = "Matthijs Klomp and Fredrik Bruzelius and Jens Nielsen and Angela Hillemyr",
booktitle = "Advances in Dynamics of Vehicles on Roads and Tracks - Proceedings of the 26th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2019",
note = "26th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2019 ; Conference date: 12-08-2019 Through 16-08-2019",
}