Deep Learning for Autonomous Driving

Burleigh, Nicholas; King, Jordan; Bräunl, Thomas

doi:10.1109/dicta47822.2019.8945818

Cited by 6 publications

(1 citation statement)

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“…Furthermore, these AVs will help form an Internet of Vehicles (IoV) which will generate big amount of data. Recently, artificial intelligence (AI), machine learning (ML) and deep learning (DL) techniques have been used to improve the state and position estimation and reference therein [15,[201][202][203]. Different AI, ML and DL frameworks have been proposed to learn and adapt these matrices online.…”

Section: Future Research Directionsmentioning

confidence: 99%

A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges

Ahangar

Ahmed

Khan

et al. 2021

Sensors

221

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The Department of Transport in the United Kingdom recorded 25,080 motor vehicle fatalities in 2019. This situation stresses the need for an intelligent transport system (ITS) that improves road safety and security by avoiding human errors with the use of autonomous vehicles (AVs). Therefore, this survey discusses the current development of two main components of an ITS: (1) gathering of AVs surrounding data using sensors; and (2) enabling vehicular communication technologies. First, the paper discusses various sensors and their role in AVs. Then, various communication technologies for AVs to facilitate vehicle to everything (V2X) communication are discussed. Based on the transmission range, these technologies are grouped into three main categories: long-range, medium-range and short-range. The short-range group presents the development of Bluetooth, ZigBee and ultra-wide band communication for AVs. The medium-range examines the properties of dedicated short-range communications (DSRC). Finally, the long-range group presents the cellular-vehicle to everything (C-V2X) and 5G-new radio (5G-NR). An important characteristic which differentiates each category and its suitable application is latency. This research presents a comprehensive study of AV technologies and identifies the main advantages, disadvantages, and challenges.

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Section: Future Research Directionsmentioning

confidence: 99%