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Lateral Motion Prediction of On-Road Preceding Vehicles: A Data-Driven Approach
Drivers’ behaviors and decision making on the road directly affect the safety of themselves, other drivers, and pedestrians. However, as distinct entities, people cannot predict the motions of surrounding vehicles and they have difficulty in performing safe reactionary driving maneuvers in a short t...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539340/ https://www.ncbi.nlm.nih.gov/pubmed/31067760 http://dx.doi.org/10.3390/s19092111 |
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author | Wang, Chen Delport, Jacques Wang, Yan |
author_facet | Wang, Chen Delport, Jacques Wang, Yan |
author_sort | Wang, Chen |
collection | PubMed |
description | Drivers’ behaviors and decision making on the road directly affect the safety of themselves, other drivers, and pedestrians. However, as distinct entities, people cannot predict the motions of surrounding vehicles and they have difficulty in performing safe reactionary driving maneuvers in a short time period. To overcome the limitations of making an immediate prediction, in this work, we propose a two-stage data-driven approach: classifying driving patterns of on-road surrounding vehicles using the Gaussian mixture models (GMM); and predicting vehicles’ short-term lateral motions (i.e., left/right turn and left/right lane change) based on real-world vehicle mobility data, provided by the U.S. Department of Transportation, with different ensemble decision trees. We considered several important kinetic features and higher order kinematic variables. The research results of our proposed approach demonstrate the effectiveness of pattern classification and on-road lateral motion prediction. This methodology framework has the potential to be incorporated into current data-driven collision warning systems, to enable more practical on-road preprocessing in intelligent vehicles, and to be applied in autopilot-driving scenarios. |
format | Online Article Text |
id | pubmed-6539340 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-65393402019-06-04 Lateral Motion Prediction of On-Road Preceding Vehicles: A Data-Driven Approach Wang, Chen Delport, Jacques Wang, Yan Sensors (Basel) Article Drivers’ behaviors and decision making on the road directly affect the safety of themselves, other drivers, and pedestrians. However, as distinct entities, people cannot predict the motions of surrounding vehicles and they have difficulty in performing safe reactionary driving maneuvers in a short time period. To overcome the limitations of making an immediate prediction, in this work, we propose a two-stage data-driven approach: classifying driving patterns of on-road surrounding vehicles using the Gaussian mixture models (GMM); and predicting vehicles’ short-term lateral motions (i.e., left/right turn and left/right lane change) based on real-world vehicle mobility data, provided by the U.S. Department of Transportation, with different ensemble decision trees. We considered several important kinetic features and higher order kinematic variables. The research results of our proposed approach demonstrate the effectiveness of pattern classification and on-road lateral motion prediction. This methodology framework has the potential to be incorporated into current data-driven collision warning systems, to enable more practical on-road preprocessing in intelligent vehicles, and to be applied in autopilot-driving scenarios. MDPI 2019-05-07 /pmc/articles/PMC6539340/ /pubmed/31067760 http://dx.doi.org/10.3390/s19092111 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Chen Delport, Jacques Wang, Yan Lateral Motion Prediction of On-Road Preceding Vehicles: A Data-Driven Approach |
title | Lateral Motion Prediction of On-Road Preceding Vehicles: A Data-Driven Approach |
title_full | Lateral Motion Prediction of On-Road Preceding Vehicles: A Data-Driven Approach |
title_fullStr | Lateral Motion Prediction of On-Road Preceding Vehicles: A Data-Driven Approach |
title_full_unstemmed | Lateral Motion Prediction of On-Road Preceding Vehicles: A Data-Driven Approach |
title_short | Lateral Motion Prediction of On-Road Preceding Vehicles: A Data-Driven Approach |
title_sort | lateral motion prediction of on-road preceding vehicles: a data-driven approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539340/ https://www.ncbi.nlm.nih.gov/pubmed/31067760 http://dx.doi.org/10.3390/s19092111 |
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