Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review

Mozaffari, Sajjad; Al-Jarrah, Omar Y.; Dianati, Mehrdad; Jennings, Paul; Mouzakitis, Alexandros

doi:10.1109/tits.2020.3012034

Cited by 395 publications

(246 citation statements)

References 57 publications

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“…In this situation, it is necessary to introduce more autonomy to the operation of the WT system. Similarly to the autonomous driving technologies [7,8], we envision future WTs having the ability to communicate and operate through machine brains making decisions autonomously in fractions of a second.…”

Section: The Bigger Picturementioning

confidence: 99%

A concept for human-cyber-physical systems of future wind turbines towards Industry 5.0

Chen¹,

Eder²,

Shihavuddin³

2020

Preprint

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This work proposes a novel concept for a semi-autonomous human-cyber-physical system (HCPS) to operate next-generation wind turbines on the way towards Industry 5.0. The exponential increase in the complexity of future wind turbines requires artificial intelligence (AI) to operate the machines efficiently and consistently. Evolving current Industry 4.0 digital twin technology beyond a sole aid for a human decision-making process, the digital twin in the proposed system is utilized for highly effective training of the AI through machine learning. Human intelligence (HI) is elevated to a supervisory level, making high-level decisions through a human-machine interface to break the autonomy when needed. This paper points out a plausible way to realize the HCPS by identifying strategic development demands for the key enabling technologies projected from readily available knowledge.

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Section: The Bigger Picturementioning

confidence: 99%

A concept for human-cyber-physical systems of future wind turbines towards Industry 5.0

Chen¹,

Eder²,

Shihavuddin³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, placing a yield sign in an intersection can change the behavior of the approaching vehicles. Hence, a comprehensive prediction module in AVs is critical to identify all position future motions to reduce collision hazards [ 12 , 13 ]. Although AD systems share many common challenges in real-world situations, they are differed noticeably in several aspects.…”

Section: Introductionmentioning

confidence: 99%

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Yeong

Velasco-Hernandez

Barry

et al. 2021

Sensors

441

133

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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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“…In this situation, it is necessary to introduce more autonomy into the operation of the WT system. Similarly, for autonomous driving technologies [14,15], we envision future WTs having the ability to communicate and operate through machine brains that can make decisions autonomously in fractions of a second. These decisions will not only rely on information from local SHM systems but also depend on supplementary information gathered from other WTs on the wind farm.…”

Section: Introduction To the Bigger Picturementioning

confidence: 99%

A Human-Cyber-Physical System toward Intelligent Wind Turbine Operation and Maintenance

et al. 2021

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This work proposes a novel concept for an intelligent and semi-autonomous human-cyber-physical system (HCPS) to operate future wind turbines in the context of Industry 5.0 technologies. The exponential increase in the complexity of next-generation wind turbines requires artificial intelligence (AI) to operate the machines efficiently and consistently. Evolving the current Industry 4.0 digital twin technology beyond a sole aid for the human decision-making process, the digital twin in the proposed system is used for highly effective training of the AI through machine learning. Human intelligence (HI) is elevated to a supervisory level, in which high-level decisions made through a human–machine interface break the autonomy, when needed. This paper also identifies and elaborates key enabling technologies (KETs) that are essential for realizing the proposed HCPS.

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Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 395 publications

References 57 publications

A concept for human-cyber-physical systems of future wind turbines towards Industry 5.0

A concept for human-cyber-physical systems of future wind turbines towards Industry 5.0

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

A Human-Cyber-Physical System toward Intelligent Wind Turbine Operation and Maintenance

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