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Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

With the significant advancement of sensor and communication technology and the reliable application of obstacle detection techniques and algorithms, automated driving is becoming a pivotal technology that can revolutionize the future of transportation and mobility. Sensors are fundamental to the pe...

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Autores principales: Yeong, De Jong, Velasco-Hernandez, Gustavo, Barry, John, Walsh, Joseph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003231/
https://www.ncbi.nlm.nih.gov/pubmed/33803889
http://dx.doi.org/10.3390/s21062140
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author Yeong, De Jong
Velasco-Hernandez, Gustavo
Barry, John
Walsh, Joseph
author_facet Yeong, De Jong
Velasco-Hernandez, Gustavo
Barry, John
Walsh, Joseph
author_sort Yeong, De Jong
collection PubMed
description With the significant advancement of sensor and communication technology and the reliable application of obstacle detection techniques and algorithms, automated driving is becoming a pivotal technology that can revolutionize the future of transportation and mobility. Sensors are fundamental to the perception of vehicle surroundings in an automated driving system, and the use and performance of multiple integrated sensors can directly determine the safety and feasibility of automated driving vehicles. Sensor calibration is the foundation block of any autonomous system and its constituent sensors and must be performed correctly before sensor fusion and obstacle detection processes may be implemented. This paper evaluates the capabilities and the technical performance of sensors which are commonly employed in autonomous vehicles, primarily focusing on a large selection of vision cameras, LiDAR sensors, and radar sensors and the various conditions in which such sensors may operate in practice. We present an overview of the three primary categories of sensor calibration and review existing open-source calibration packages for multi-sensor calibration and their compatibility with numerous commercial sensors. We also summarize the three main approaches to sensor fusion and review current state-of-the-art multi-sensor fusion techniques and algorithms for object detection in autonomous driving applications. The current paper, therefore, provides an end-to-end review of the hardware and software methods required for sensor fusion object detection. We conclude by highlighting some of the challenges in the sensor fusion field and propose possible future research directions for automated driving systems.
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spelling pubmed-80032312021-03-28 Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review Yeong, De Jong Velasco-Hernandez, Gustavo Barry, John Walsh, Joseph Sensors (Basel) Review With the significant advancement of sensor and communication technology and the reliable application of obstacle detection techniques and algorithms, automated driving is becoming a pivotal technology that can revolutionize the future of transportation and mobility. Sensors are fundamental to the perception of vehicle surroundings in an automated driving system, and the use and performance of multiple integrated sensors can directly determine the safety and feasibility of automated driving vehicles. Sensor calibration is the foundation block of any autonomous system and its constituent sensors and must be performed correctly before sensor fusion and obstacle detection processes may be implemented. This paper evaluates the capabilities and the technical performance of sensors which are commonly employed in autonomous vehicles, primarily focusing on a large selection of vision cameras, LiDAR sensors, and radar sensors and the various conditions in which such sensors may operate in practice. We present an overview of the three primary categories of sensor calibration and review existing open-source calibration packages for multi-sensor calibration and their compatibility with numerous commercial sensors. We also summarize the three main approaches to sensor fusion and review current state-of-the-art multi-sensor fusion techniques and algorithms for object detection in autonomous driving applications. The current paper, therefore, provides an end-to-end review of the hardware and software methods required for sensor fusion object detection. We conclude by highlighting some of the challenges in the sensor fusion field and propose possible future research directions for automated driving systems. MDPI 2021-03-18 /pmc/articles/PMC8003231/ /pubmed/33803889 http://dx.doi.org/10.3390/s21062140 Text en © 2021 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 Review
Yeong, De Jong
Velasco-Hernandez, Gustavo
Barry, John
Walsh, Joseph
Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review
title Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review
title_full Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review
title_fullStr Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review
title_full_unstemmed Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review
title_short Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review
title_sort sensor and sensor fusion technology in autonomous vehicles: a review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003231/
https://www.ncbi.nlm.nih.gov/pubmed/33803889
http://dx.doi.org/10.3390/s21062140
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