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.
Search-Based Motion Planning for Performance Autonomous Driving
Lect.Notes Mechanical Engineering
The IAVSD International Symposium on Dynamics of Vehicles on Roads and Tracks ; 2019 ; Gothenburg, Sweden August 12, 2019 - August 16, 2019
2020-02-13
11 pages
Article/Chapter (Book)
Electronic Resource
English
Search-Based Motion Planning for Performance Automonom Driving
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