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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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2083-2567
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A Machine Learning Approach for the Segmentation of Driving Maneuvers and its Application in Autonomous Parking

Gennaro Notomista
  • Corresponding author
  • Universit à degli Studi di Napoli “Federerico II”, Via Claudio 21, 80125 Napoli, Italy
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Michael Botsch
Published Online: 2017-05-03 | DOI: https://doi.org/10.1515/jaiscr-2017-0017

Abstract

A classification system for the segmentation of driving maneuvers and its validation in autonomous parking using a small-scale vehicle are presented in this work. The classifiers are designed to detect points that are crucial for the path-planning task, thus enabling the implementation of efficient autonomous parking maneuvers. The training data set is generated by simulations using appropriate vehicle-dynamics models and the resulting classifiers are validated with the small-scale autonomous vehicle. To achieve both a high classification performance and a classification system that can be implemented on a microcontroller with limited computational resources, a two-stage design process is applied. In a first step an ensemble classifier, the Random Forest (RF) algorithm, is constructed and based on the RF-kernel a General Radial Basis Function (GRBF) classifier is generated. The GRBF-classifier is integrated into the small-scale autonomous vehicle leading to excellent performance in parallel-, cross- and oblique-parking maneuvers. The work shows that segmentation using classifies and open-loop control are an efficient approach in autonomous driving for the implementation of driving maneuvers.

Keywords: autonomous parking; ensemble learning; maneuver segmentation

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About the article

Received: 2016-04-14

Accepted: 2016-11-14

Published Online: 2017-05-03

Published in Print: 2017-10-01


Citation Information: Journal of Artificial Intelligence and Soft Computing Research, Volume 7, Issue 4, Pages 243–255, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2017-0017.

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© 2017. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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