2019
DOI: 10.1177/1550147719883133
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A deep learning platooning-based video information-sharing Internet of Things framework for autonomous driving systems

Abstract: To enhance the safety and stability of autonomous vehicles, we present a deep learning platooning-based video information-sharing Internet of Things framework in this study. The proposed Internet of Things framework incorporates concepts and mechanisms from several domains of computer science, such as computer vision, artificial intelligence, sensor technology, and communication technology. The information captured by camera, such as road edges, traffic lights, and zebra lines, is highlighted using computer vi… Show more

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Cited by 11 publications
(6 citation statements)
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References 27 publications
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“…In order to address the risk of collision at such small inter-vehicle distances, Dharshan et al [39] present an experimentally validated approach to optimize the braking system of autonomous vehicle platoon considering the vehicle loading condition for executing a collision free braking maneuver. Moreover, the close/narrow inter-vehicle distance, used in vehicle platoon studies [28], [38] and in this study, have been substantiated by the recent advancements in autonomous vehicle ecological cooperative control [40], [41] coupled with IoT [42], [43] to ensure passenger safety. Models deployed for drag coefficient prediction include Artificial Neural Networks, Regression models and Support Vector Regression.…”
Section: A Aerodynamic Study Of Vehicle Platoonmentioning
confidence: 82%
“…In order to address the risk of collision at such small inter-vehicle distances, Dharshan et al [39] present an experimentally validated approach to optimize the braking system of autonomous vehicle platoon considering the vehicle loading condition for executing a collision free braking maneuver. Moreover, the close/narrow inter-vehicle distance, used in vehicle platoon studies [28], [38] and in this study, have been substantiated by the recent advancements in autonomous vehicle ecological cooperative control [40], [41] coupled with IoT [42], [43] to ensure passenger safety. Models deployed for drag coefficient prediction include Artificial Neural Networks, Regression models and Support Vector Regression.…”
Section: A Aerodynamic Study Of Vehicle Platoonmentioning
confidence: 82%
“…• Distributed learning: local gradient computation [64][65][66][67][68][69] , over-the-air computing [70][71][72][73] , importance-aware RRM [74][75][76][77] , differential privacy [78,79] • Split inference: feature extraction [80][81][82][83][84] , importance-aware RRM [81,[85][86][87][88][89] , SplitNet approach [90][91][92][93] • Distributed consensus: local-sate estimation and prediction [94][95][96] , SDT [97] , PBFT consensus [98] , vehicle platooning [99][100][101] , Blockchain [97,102] • Machine-vision cameras: ROI-based effectiveness encoding [103,104] , camera-side feature extraction [105] , production-line inspection [106] , surveillance [103,107]<...…”
Section: H2m Semcommentioning
confidence: 99%
“…• Distributed consensus: Local-sate estimation and prediction [93][94][95], SDT [96], PBFT consensus [97], Vehicle platooning [98][99][100], Blockchain [96,101].…”
Section: M2m Semcommentioning
confidence: 99%
“…Most recently, deep learning have been adopted to empower platooning. Essentially, CNN-based effectiveness encoders are designed to intelligently extract information from real-time videos captured by on-board cameras, such as traffic lights, lanes and obstacles [95]. Exchanging such sensing data and use them for consensus on complex manoeuvres give the platoon collective intelligence for autodriving.…”
Section: Vehicle Platooningmentioning
confidence: 99%