Special Issue "Intelligent Transportation Systems (ITS)"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 30 April 2020

Special Issue Editor

Guest Editor
Prof. Dr. Beatriz L. Boada

Mechanical Engineering Department, Institute for Automotive Vehicle Safety (ISVA), Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain
Website | E-Mail
Interests: vehicle control; vehicle safety; internet of things, sensor fusion, intelligent vehicles

Special Issue Information

Dear Colleagues,

In the last few years, the growth of the number of vehicles in cities has caused problems of mobility, environmental pollution, and road safety. The intelligent transportation system (ITS) concept includes many advanced technologies, such as, communication, sensing, and control, which are used for managing a high amount of information, in order to face these challenges. ITS is a multidisciplinary field that comprises a large number of research areas.

Although tremendous advancements have been made in the last decade in this field, there are still aspects that need to be addressed in order to improve the transportation safety, efficiency, and sustainability.

The topics of interest include, but are not limited to, the following:

  • Internet of things/connected vehicles
  • Big data
  • Electric/autonomous vehicles
  • Vehicle control
  • Traffic control/traffic management
  • Smart sensors
  • Smart mobility systems
  • Reliability and security in transport

Prof. Dr. Beatriz L. Boada
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Internet of things/connected vehicles 
  • Big data
  • Electric/autonomous vehicles 
  • Vehicle control
  • Traffic control/traffic management
  • Smart sensors 
  • Smart mobility systems 
  • Reliability and security in transport

Published Papers (2 papers)

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Research

Open AccessArticle
Decision-Making System for Lane Change Using Deep Reinforcement Learning in Connected and Automated Driving
Electronics 2019, 8(5), 543; https://doi.org/10.3390/electronics8050543
Received: 4 April 2019 / Revised: 7 May 2019 / Accepted: 10 May 2019 / Published: 14 May 2019
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Abstract
Lane changing systems have consistently received attention in the fields of vehicular communication and autonomous vehicles. In this paper, we propose a lane change system that combines deep reinforcement learning and vehicular communication. A host vehicle, trying to change lanes, receives the state [...] Read more.
Lane changing systems have consistently received attention in the fields of vehicular communication and autonomous vehicles. In this paper, we propose a lane change system that combines deep reinforcement learning and vehicular communication. A host vehicle, trying to change lanes, receives the state information of the host vehicle and a remote vehicle that are both equipped with vehicular communication devices. A deep deterministic policy gradient learning algorithm in the host vehicle determines the high-level action of the host vehicle from the state information. The proposed system learns straight-line driving and collision avoidance actions without vehicle dynamics knowledge. Finally, we consider the update period for the state information from the host and remote vehicles. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems (ITS))
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Open AccessArticle
A Vehicle Type Dependent Car-following Model Based on Naturalistic Driving Study
Electronics 2019, 8(4), 453; https://doi.org/10.3390/electronics8040453
Received: 8 March 2019 / Revised: 17 April 2019 / Accepted: 19 April 2019 / Published: 23 April 2019
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Abstract
In this paper, a car-following model considering the preceding vehicle type is proposed to describe the longitudinal driving behavior closer to reality. Based on the naturalistic driving data sampled in real traffic for more than half a year, the relation between ego vehicle [...] Read more.
In this paper, a car-following model considering the preceding vehicle type is proposed to describe the longitudinal driving behavior closer to reality. Based on the naturalistic driving data sampled in real traffic for more than half a year, the relation between ego vehicle velocity and relative distance was analyzed by a multi-variable Gaussian Mixture model, from which it is found that the driver following behavior is influenced by the type of leading vehicle. Then a Hidden Markov model was designed to identify the vehicle type. This car-following model was trained and tested by using the naturalistic driving data. It can identify the leading vehicle type, i.e., passenger car, bus, and truck, and predict the ego vehicle velocity and relative distance based on a series of limited historical data in real time. The experimental validation results show that the identification accuracy of vehicle type under the static and dynamical conditions are 96.6% and 83.1%, respectively. Furthermore, comparing the results with the well-known collision avoidance model and intelligent driver model show that this new model is more accurate and can be used to design advanced driver assist systems for better adaptability to traffic conditions. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems (ITS))
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