<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></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.

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

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

A classification system for the segmentation of driving maneuvers and its validation in autonomous parking using a small-scale vehicle are presented in this...

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

Publié en ligne: 03 mai 2017

Pages: 243 - 255

Reçu: 14 avr. 2016

Accepté: 14 nov. 2016

DOI: https://doi.org/10.1515/jaiscr-2017-0017

Mots clés
autonomous parking, ensemble learning, maneuver segmentation

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Publié en ligne: 03 mai 2017

Pages: 243 - 255

Reçu: 14 avr. 2016

Accepté: 14 nov. 2016

DOI: https://doi.org/10.1515/jaiscr-2017-0017

Mots clésautonomous parking, ensemble learning, maneuver segmentation

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
autonomous parking, ensemble learning, maneuver segmentation