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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| Published in | Sensors (Basel, Switzerland) Vol. 21; no. 6; p. 2140 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
MDPI
18.03.2021
MDPI AG |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| Author | Yeong, De Jong Velasco-Hernandez, Gustavo Walsh, Joseph Barry, John |
| AuthorAffiliation | 2 School of Science Technology, Engineering and Mathematics, Munster Technological University, V92 CX88 Tralee, Ireland 1 IMaR Research Centre, Munster Technological University, V92 CX88 Tralee, Ireland; gustavo.velascohernandez@staff.ittralee.ie (G.V.-H.); john.barry@staff.ittralee.ie (J.B.); joseph.walsh@staff.ittralee.ie (J.W.) 3 Lero—Science Foundation Ireland Research Centre for Software, V92 NYD3 Limerick, Ireland |
| AuthorAffiliation_xml | – name: 3 Lero—Science Foundation Ireland Research Centre for Software, V92 NYD3 Limerick, Ireland – name: 2 School of Science Technology, Engineering and Mathematics, Munster Technological University, V92 CX88 Tralee, Ireland – name: 1 IMaR Research Centre, Munster Technological University, V92 CX88 Tralee, Ireland; gustavo.velascohernandez@staff.ittralee.ie (G.V.-H.); john.barry@staff.ittralee.ie (J.B.); joseph.walsh@staff.ittralee.ie (J.W.) |
| Author_xml | – sequence: 1 givenname: De Jong orcidid: 0000-0002-4626-8040 surname: Yeong fullname: Yeong, De Jong – sequence: 2 givenname: Gustavo orcidid: 0000-0002-2177-6348 surname: Velasco-Hernandez fullname: Velasco-Hernandez, Gustavo – sequence: 3 givenname: John surname: Barry fullname: Barry, John – sequence: 4 givenname: Joseph orcidid: 0000-0002-6756-3700 surname: Walsh fullname: Walsh, Joseph |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33803889$$D View this record in MEDLINE/PubMed |
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| Copyright | 2021 by the authors. 2021 |
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| Keywords | autonomous vehicles radar obstacle detection sensor fusion lidar camera calibration perception self-driving cars |
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