VOLUME 13(2) • 2021
Autonomous Driving Algorithms and Behaviors, Sensing and Computing Technologies, and Connected Vehicle Data in Smart Transportation Networks
ABSTRACT. This article presents an empirical study carried out to evaluate and analyze autonomous driving algorithms and behaviors, sensing and computing technologies, and connected vehicle data in smart transportation networks. Building our argument by drawing on data collected from AAA, AUVSI, CAR..
Autonomous Vehicle Driving Algorithms, Deep Learning-based Sensing Technologies, and Big Geospatial Data Analytics in Smart Sustainable Intelligent Transportation Systems
ABSTRACT. We draw on a substantial body of theoretical and empirical research on autonomous vehicle driving algorithms, deep learning-based sensing technologies, and big geospatial data analytics in smart sustainable intelligent transportation systems, and to explore this, we inspected, used, and re..
Connected Vehicle Technologies, Autonomous Driving Perception Algorithms, and Smart Sustainable Urban Mobility Behaviors in Networked Transport Systems
ABSTRACT. The aim of this paper is to synthesize and analyze existing evidence on connected vehicle technologies, autonomous driving perception algorithms, and smart sustainable urban mobility behaviors in networked transport systems. Using and replicating data from AAA, Abraham et al. (2017), Adobe..
Intelligent Transportation Applications, Autonomous Vehicle Perception Sensor Data, and Decision-Making Self-Driving Car Control Algorithms in Smart Sustainable Urban Mobility Systems
ABSTRACT. Based on an in-depth survey of the literature, the purpose of the paper is to explore intelligent transportation applications, autonomous vehicle perception sensor data, and decision-making self-driving car control algorithms in smart sustainable urban mobility systems. Using and replicati..
Smart Traffic Planning and Analytics, Autonomous Mobility Technologies, and Algorithm-driven Sensing Devices in Urban Transportation Systems
ABSTRACT. Despite the relevance of smart traffic planning and analytics, autonomous mobility technologies, and algorithm-driven sensing devices in urban transportation systems, only limited research has been conducted on this topic. Using and replicating data from AAA, ANSYS, Atomik Research, AUVSI,..
Predictive Control Algorithms, Real-World Connected Vehicle Data, and Smart Mobility Technologies in Intelligent Transportation Planning and Engineering
ABSTRACT. Employing recent research results covering predictive control algorithms, real-world connected vehicle data, and smart mobility technologies in intelligent transportation planning and engineering, and building our argument by drawing on data collected from Brookings, Capgemini, Ipsos, Jone..
Autonomous Vehicle Decision-Making Algorithms, Interconnected Sensor Networks, and Big Geospatial Data Analytics in Smart Urban Mobility Systems
ABSTRACT. We develop a conceptual framework based on a systematic and comprehensive literature review on autonomous vehicle decision-making algorithms, interconnected sensor networks, and big geospatial data analytics in smart urban mobility systems. Building our argument by drawing on data collecte..
Intelligent Vehicular Networks, Deep Learning-based Sensing Technologies, and Big Data-driven Algorithmic Decision-Making in Smart Transportation Systems
ABSTRACT. This paper analyzes the outcomes of an exploratory review of the current research on intelligent vehicular networks, deep learning-based sensing technologies, and big data-driven algorithmic decision-making in smart transportation systems. The data used for this study was obtained and repl..
Autonomous Vehicle Routing and Navigation, Computer Vision Algorithms, and Transportation Analytics in Network Connectivity Systems
ABSTRACT. Empirical evidence on autonomous vehicle routing and navigation, computer vision algorithms, and transportation analytics in network connectivity systems has been scarcely documented in the literature. Using and replicating data from ANSYS, Atomik Research, APA, AUDI AG, BCG, Capgemini, EY..
Autonomous Vehicle Interaction Control Software, Big Geospatial Data Analytics, and Networked Driverless Technologies in Smart Sustainable Urban Transport Systems
ABSTRACT. The purpose of this study was to empirically examine autonomous vehicle interaction control software, big geospatial data analytics, and networked driverless technologies in smart sustainable urban transport systems. Building our argument by drawing on data collected from ANSYS, APA, Atomi..