Maneuver segmentation for autonomous parking based on ensemble learning | IEEE Conference Publication | IEEE Xplore

Maneuver segmentation for autonomous parking based on ensemble learning


Abstract:

A classification system for the segmentation of parking maneuvers and its validation using a small-scale autonomous vehicle are presented in this work. The classifiers ar...Show More

Abstract:

A classification system for the segmentation of parking maneuvers and its validation using a small-scale autonomous 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 an excellent performance in both parallel- and cross-parking maneuvers.
Date of Conference: 12-17 July 2015
Date Added to IEEE Xplore: 01 October 2015
ISBN Information:

ISSN Information:

Conference Location: Killarney, Ireland

Contact IEEE to Subscribe

References

References is not available for this document.