Research Area: Machine Learning
This paper presents a novel approach to teach a vehicle how to drift, in a similar manner that professional drivers do. Specifically, a hybrid structure formed by a Model Predictive Controller and feedforward Neural Networks is employed for this purpose. The novelty of this work lies in a) the adoption of a data-based approach to achieve autonomous drifting along a wide range of road radii and body slip angles, and b) in the implementation of a road terrain classifier to adjust the system actuation depending on the current friction characteristics. The presented drift control system is implemented in a multi-actuated ground vehicle equipped with active front steering and in-wheel electric motors and trained to drift by a real test driver using a driver-in-the-loop setup. Its performance is verified in the simulation environment IPG-CarMaker through different open loop and path following drifting manoeuvres.
Keywords:
Vehicle
Autonomously Drift
Neural Networks
Machine Learning
Deep Learning
Author(s) Name: Manuel Acosta and Stratis Kanarachos
Journal name: Knowledge-Based Systems
Conferrence name:
Publisher name: ELSEVIER
DOI: 10.1016/j.knosys.2018.04.015
Volume Information: Volume 153, 1 August 2018, Pages 12-28
