Decision-Making Self-Driving Car Control Algorithms: Intelligent Transportation Systems, Sensing and Computing Technologies, and Connected Autonomous Vehicles

2019 ◽  
Vol 11 (2) ◽  
pp. 29 ◽  
Keyword(s):  
Decision Making ◽  
Intelligent Transportation Systems ◽  
Autonomous Vehicles ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Control Algorithms ◽  
Computing Technologies

scholarly journals How Can Smart Mobility Innovations Alleviate Transportation Disadvantage? Assembling a Conceptual Framework through a Systematic Review

2020 ◽  
Vol 10 (18) ◽  
pp. 6306 ◽  
Author(s):  
Luke Butler ◽  
Tan Yigitcanlar ◽  
Alexander Paz
Keyword(s):  
Conceptual Framework ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Autonomous Vehicles ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Technological Innovations ◽  
Potential Solution ◽  
Wide Range ◽  
Smart Mobility

Transportation disadvantage is about the difficulty accessing mobility services required to complete activities associated with employment, shopping, business, essential needs, and recreation. Technological innovations in the field of smart mobility have been identified as a potential solution to help individuals overcome issues associated with transportation disadvantage. This paper aims to provide a consolidated understanding on how smart mobility innovations can contribute to alleviate transportation disadvantage. A systematic literature review is completed, and a conceptual framework is developed to provide the required information to address transportation disadvantage. The results are categorized under the physical, economic, spatial, temporal, psychological, information, and institutional dimensions of transportation disadvantage. The study findings reveal that: (a) Primary smart mobility innovations identified in the literature are demand responsive transportation, shared transportation, intelligent transportation systems, electric mobility, autonomous vehicles, and Mobility-as-a-Services. (b) Smart mobility innovations could benefit urban areas by improving accessibility, efficiency, coverage, flexibility, safety, and the overall integration of the transportation system. (c) Smart mobility innovations have the potential to contribute to the alleviation of transportation disadvantage. (d) Mobility-as-a-Service has high potential to alleviate transportation disadvantage primarily due to its ability to integrate a wide-range of services.

Intelligent transportation system for sustainable environment in smart cities

2021 ◽  
pp. 002072092098350
Author(s):  
Liang Zhao ◽  
Yuanhua Jia
Keyword(s):  
Environmental Sustainability ◽  
Intelligent Transportation Systems ◽  
Autonomous Vehicles ◽  
Intelligent Transportation System ◽  
Smart Cities ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Advanced Technology ◽  
Toxic Substances ◽  
Sustainable Environment

Advanced technology has ushered in the urge to enhance the travel experience. Besides the consistent desire to travel faster and more comfortably, the need to ensure transportation sustainability has remained constant. Smart cities employ top-grade technological applications to facilitate operations. Intelligent transportation systems involve the use of advanced transportation technologies. Through the integration of the Internet of Vehicles, cars in traffic can send and receive data between themselves and other vehicles and the environment. This data is processed to ensure efficient transportation by controlling traffic flows and preventing accidents. In this study, a literature review is conducted on how intelligent transportation systems contribute to environmental sustainability in smart cities. With technologies such as electricity-driven cars and autonomous vehicles, the systems minimize the emission of toxic substances to the environment while enhancing the interaction of the car with its surroundings to avoid accidents.

scholarly journals Development of a Decision Making Algorithm for Traffic Jams Reduction Applied to Intelligent Transportation Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-16
Author(s):  
David Gómez ◽  
José-Fernán Martínez ◽  
Juana Sendra ◽  
Gregorio Rubio
Keyword(s):  
Decision Making ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Analytic Hierarchy ◽  
Traffic Jams ◽  
Traffic Management System ◽  
Smart Traffic ◽  
Hierarchy Process

This paper is aimed at developing a decision making algorithm for traffic jams reduction that can be applied to Intelligent Transportation Systems. To do so, these algorithms must address two main challenges that arise in this context. On one hand, there are uncertainties in the data received from sensor networks produced by incomplete information or because the information loses some of the precision during information processing and display. On the other hand, there is the variability of the context in which these types of systems are operating. More specifically, Analytic Hierarchy Process (AHP) algorithm has been adapted to ITS, taking into account the mentioned challenges. After explaining the proposed decision making method, it is validated in a specific scenario: a smart traffic management system.

scholarly journals The Role of 5G Technologies: Challenges in Smart Cities and Intelligent Transportation Systems

2020 ◽  
Vol 12 (16) ◽  
pp. 6469 ◽  
Author(s):  
Leonardo Guevara ◽  
Fernando Auat Cheein
Keyword(s):  
Intelligent Transportation Systems ◽  
Autonomous Vehicles ◽  
Smart Cities ◽  
New Technology ◽  
High Mobility ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Fully Integrated ◽  
Legal Challenges ◽  
Current Scenario

The new mobile technology, 5G, challenges the current scenario in communications by overcoming the flaws of currently working 4G. Such new technology offers to smart cities and intelligent transportation systems a new way to become fully integrated by allowing massive simultaneous connections and ubiquity of network, even under high mobility situations or dense populated areas. In this way, 5G will become a key enabler for real Internet of Things and its corresponding Internet of Vehicles. This discussion is aimed at providing, in a comprehensive manner, how 5G technology will impact on smart cities, intelligent transportation systems –including autonomous or semi-autonomous vehicles– and vehicular communications, its technical, economic and legal challenges, in the following years.

scholarly journals Planning and Decision-making for Connected Autonomous Vehicles at Road Intersections: A Review

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Shen Li ◽  
Keqi Shu ◽  
Chaoyi Chen ◽  
Dongpu Cao
Keyword(s):  
Decision Making ◽  
Intelligent Transportation Systems ◽  
Autonomous Vehicles ◽  
Research Problem ◽  
Transportation Systems ◽  
Automated Driving ◽  
Simulation Tools ◽  
The Future ◽  
Research Problems ◽  
Future Direction

AbstractPlanning and decision-making technology at intersections is a comprehensive research problem in intelligent transportation systems due to the uncertainties caused by a variety of traffic participants. As wireless communication advances, vehicle infrastructure integrated algorithms designed for intersection planning and decision-making have received increasing attention. In this paper, the recent studies on the planning and decision-making technologies at intersections are primarily overviewed. The general planning and decision-making approaches are presented, which include graph-based approach, prediction base approach, optimization-based approach and machine learning based approach. Since connected autonomous vehicles (CAVs) is the future direction for the automated driving area, we summarized the evolving planning and decision-making methods based on vehicle infrastructure cooperative technologies. Both four-way signalized and unsignalized intersection(s) are investigated under purely automated driving traffic and mixed traffic. The study benefit from current strategies, protocols, and simulation tools to help researchers identify the presented approaches’ challenges and determine the research gaps, and several remaining possible research problems that need to be solved in the future.

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