Journal Article10.1109/COMST.2018.2888904
Networking and Communications in Autonomous Driving: A Survey
Jiadai Wang,Jiajia Liu,Nei Kato +2 more
509
TL;DR: This paper surveys the networking and communication technologies in autonomous driving from two aspects: intra- and inter-vehicle.
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Abstract: The development of light detection and ranging, Radar, camera, and other advanced sensor technologies inaugurated a new era in autonomous driving. However, due to the intrinsic limitations of these sensors, autonomous vehicles are prone to making erroneous decisions and causing serious disasters. At this point, networking and communication technologies can greatly make up for sensor deficiencies, and are more reliable, feasible and efficient to promote the information interaction, thereby improving autonomous vehicle’s perception and planning capabilities as well as realizing better vehicle control. This paper surveys the networking and communication technologies in autonomous driving from two aspects: intra- and inter-vehicle. The intra-vehicle network as the basis of realizing autonomous driving connects the on-board electronic parts. The inter-vehicle network is the medium for interaction between vehicles and outside information. In addition, we present the new trends of communication technologies in autonomous driving, as well as investigate the current mainstream verification methods and emphasize the challenges and open issues of networking and communications in autonomous driving.
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Citations
Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges
TL;DR: Some typical application scenarios of edge computing in IIoT, such as prognostics and health management, smart grids, manufacturing coordination, intelligent connected vehicles (ICV), and smart logistics, are introduced.
656
Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches
Fengxiao Tang,Yuichi Kawamoto,Nei Kato,Jiajia Liu +3 more
- 01 Feb 2020
TL;DR: A survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration.
617
Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions
TL;DR: This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-the-art strategies for safe AD, with their major achievements and limitations.
Security and privacy in 6G networks: New areas and new challenges
TL;DR: This survey discusses four key aspects of 6G networks – real-time intelligent edge computing, distributed artificial intelligence, intelligent radio, and 3D intercoms – and some promising emerging technologies in each area, along with the relevant security and privacy issues.
327
Flight Delay Prediction Based on Aviation Big Data and Machine Learning
TL;DR: A broader scope of factors which may potentially influence the flight delay is explored, and several machine learning-based models in designed generalized flight delay prediction tasks are compared, and the proposed random forest-based model can obtain higher prediction accuracy and overcome the overfitting problem.
297
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