Example of IEEE Intelligent Transportation Systems Magazine format
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Example of IEEE Intelligent Transportation Systems Magazine format Example of IEEE Intelligent Transportation Systems Magazine format Example of IEEE Intelligent Transportation Systems Magazine format Example of IEEE Intelligent Transportation Systems Magazine format Example of IEEE Intelligent Transportation Systems Magazine format Example of IEEE Intelligent Transportation Systems Magazine format Example of IEEE Intelligent Transportation Systems Magazine format Example of IEEE Intelligent Transportation Systems Magazine format Example of IEEE Intelligent Transportation Systems Magazine format
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Example of IEEE Intelligent Transportation Systems Magazine format Example of IEEE Intelligent Transportation Systems Magazine format Example of IEEE Intelligent Transportation Systems Magazine format Example of IEEE Intelligent Transportation Systems Magazine format Example of IEEE Intelligent Transportation Systems Magazine format Example of IEEE Intelligent Transportation Systems Magazine format Example of IEEE Intelligent Transportation Systems Magazine format Example of IEEE Intelligent Transportation Systems Magazine format Example of IEEE Intelligent Transportation Systems Magazine format
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This content is only for preview purposes. The original open access content can be found here.
open access Open Access
recommended Recommended

IEEE Intelligent Transportation Systems Magazine — Template for authors

Publisher: IEEE
Categories Rank Trend in last 3 yrs
Mechanical Engineering #36 of 596 down down by 1 rank
Automotive Engineering #8 of 95 down down by 2 ranks
Computer Science Applications #59 of 693 up up by 6 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 191 Published Papers | 1671 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 25/06/2020
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Related Journals

open access Open Access
recommended Recommended

IEEE

Quality:  
High
CiteRatio: 13.5
SJR: 1.591
SNIP: 3.204
open access Open Access

Taylor and Francis

Quality:  
High
CiteRatio: 6.4
SJR: 0.884
SNIP: 1.244
open access Open Access
recommended Recommended

Taylor and Francis

Quality:  
High
CiteRatio: 6.8
SJR: 1.321
SNIP: 1.764

Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

8.7

32% from 2019

CiteRatio for IEEE Intelligent Transportation Systems Magazine from 2016 - 2020
Year Value
2020 8.7
2019 6.6
2018 5.7
2017 6.1
2016 5.8
graph view Graph view
table view Table view

0.831

1% from 2019

SJR for IEEE Intelligent Transportation Systems Magazine from 2016 - 2020
Year Value
2020 0.831
2019 0.82
2018 0.574
2017 0.816
2016 0.441
graph view Graph view
table view Table view

2.366

17% from 2019

SNIP for IEEE Intelligent Transportation Systems Magazine from 2016 - 2020
Year Value
2020 2.366
2019 2.03
2018 2.063
2017 2.628
2016 2.105
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 32% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has increased by 1% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 17% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

IEEE Intelligent Transportation Systems Magazine

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IEEE

IEEE Intelligent Transportation Systems Magazine

Approved by publishing and review experts on SciSpace, this template is built as per for IEEE Intelligent Transportation Systems Magazine formatting guidelines as mentioned in IEEE author instructions. The current version was created on 24 Jun 2020 and has been used by 197 authors to write and format their manuscripts to this journal.

Engineering

i
Last updated on
24 Jun 2020
i
ISSN
1939-1390
i
Impact Factor
High - 1.826
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
IEEEtran
i
Citation Type
Numbered
[25]
i
Bibliography Example
C. W. J. Beenakker, “Specular andreev reflection in graphene,” Phys. Rev. Lett., vol. 97, no. 6, p.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1109/MITS.2010.939925
A Tutorial on Graph-Based SLAM
Giorgio Grisetti, Rainer Kümmerle1, Cyrill Stachniss1, Wolfram Burgard1

Abstract:

Being able to build a map of the environment and to simultaneously localize within this map is an essential skill for mobile robots navigating in unknown environments in absence of external referencing systems such as GPS. This so-called simultaneous localization and mapping (SLAM) problem has been one of the most popular res... Being able to build a map of the environment and to simultaneously localize within this map is an essential skill for mobile robots navigating in unknown environments in absence of external referencing systems such as GPS. This so-called simultaneous localization and mapping (SLAM) problem has been one of the most popular research topics in mobile robotics for the last two decades and efficient approaches for solving this task have been proposed. One intuitive way of formulating SLAM is to use a graph whose nodes correspond to the poses of the robot at different points in time and whose edges represent constraints between the poses. The latter are obtained from observations of the environment or from movement actions carried out by the robot. Once such a graph is constructed, the map can be computed by finding the spatial configuration of the nodes that is mostly consistent with the measurements modeled by the edges. In this paper, we provide an introductory description to the graph-based SLAM problem. Furthermore, we discuss a state-of-the-art solution that is based on least-squares error minimization and exploits the structure of the SLAM problems during optimization. The goal of this tutorial is to enable the reader to implement the proposed methods from scratch. read more read less

Topics:

Graph (abstract data type) (59%)59% related to the paper, Graph theory (55%)55% related to the paper, Mobile robot (55%)55% related to the paper, Simultaneous localization and mapping (55%)55% related to the paper, Motion planning (52%)52% related to the paper
View PDF
1,103 Citations
open accessOpen access Journal Article DOI: 10.1109/MITS.2014.2306552
Making Bertha Drive?An Autonomous Journey on a Historic Route

Abstract:

125 years after Bertha Benz completed the first overland journey in automotive history, the Mercedes Benz S-Class S 500 INTELLIGENT DRIVE followed the same route from Mannheim to Pforzheim, Germany, in fully autonomous manner. The autonomous vehicle was equipped with close-to-production sensor hardware and relied solely on vi... 125 years after Bertha Benz completed the first overland journey in automotive history, the Mercedes Benz S-Class S 500 INTELLIGENT DRIVE followed the same route from Mannheim to Pforzheim, Germany, in fully autonomous manner. The autonomous vehicle was equipped with close-to-production sensor hardware and relied solely on vision and radar sensors in combination with accurate digital maps to obtain a comprehensive understanding of complex traffic situations. The historic Bertha Benz Memorial Route is particularly challenging for autonomous driving. The course taken by the autonomous vehicle had a length of 103 km and covered rural roads, 23 small villages and major cities (e.g. downtown Mannheim and Heidelberg). The route posed a large variety of difficult traffic scenarios including intersections with and without traffic lights, roundabouts, and narrow passages with oncoming traffic. This paper gives an overview of the autonomous vehicle and presents details on vision and radar-based perception, digital road maps and video-based self-localization, as well as motion planning in complex urban scenarios. read more read less
View PDF
783 Citations
Journal Article DOI: 10.1109/MITS.2016.2583491
Autonomous Vehicle Safety: An Interdisciplinary Challenge
Philip Koopman1, Michael Wagner

Abstract:

Ensuring the safety of fully autonomous vehicles requires a multi-disciplinary approach across all the levels of functional hierarchy, from hardware fault tolerance, to resilient machine learning, to cooperating with humans driving conventional vehicles, to validating systems for operation in highly unstructured environments,... Ensuring the safety of fully autonomous vehicles requires a multi-disciplinary approach across all the levels of functional hierarchy, from hardware fault tolerance, to resilient machine learning, to cooperating with humans driving conventional vehicles, to validating systems for operation in highly unstructured environments, to appropriate regulatory approaches. Significant open technical challenges include validating inductive learning in the face of novel environmental inputs and achieving the very high levels of dependability required for full-scale fleet deployment. However, the biggest challenge may be in creating an end-to-end design and deployment process that integrates the safety concerns of a myriad of technical specialties into a unified approach. read more read less

Topics:

Dependability (54%)54% related to the paper, Software deployment (51%)51% related to the paper
418 Citations
open accessOpen access Journal Article DOI: 10.1109/MITS.2012.2189969
Vision Enhancement in Homogeneous and Heterogeneous Fog
J-P Tarel, Nicolas Hautiere, Laurent Caraffa, Aurelien Cord1, Houssam Halmaoui1, Dominique Gruyer1

Abstract:

One source of accidents when driving a vehicle is the presence of fog. Fog fades the colors and reduces the contrasts in the scene with respect to their distances from the driver. Various camera-based Advanced Driver Assistance Systems (ADAS) can be improved if efficient algorithms are designed for visibility enhancement in r... One source of accidents when driving a vehicle is the presence of fog. Fog fades the colors and reduces the contrasts in the scene with respect to their distances from the driver. Various camera-based Advanced Driver Assistance Systems (ADAS) can be improved if efficient algorithms are designed for visibility enhancement in road images. The visibility enhancement algorithm proposed in [1] is not optimized for road images. In this paper, we reformulate the problem as the inference of the local atmospheric veil from constraints. The algorithm in [1] thus becomes a particular case. From this new derivation, we propose to better handle road images by introducing an extra constraint taking into account that a large part of the image can be assumed to be a planar road. The advantages of the proposed local algorithm are the speed, the possibility to handle both color and gray-level images, and the small number of parameters. A new scheme is proposed for rating visibility enhancement algorithms based on the addition of several types of generated fog on synthetic and camera images. A comparative study and quantitative evaluation with other state-of-the-art algorithms is thus proposed. This evaluation demonstrates that the new algorithm produces better results with homogeneous fog and that it is able to deal better with the presence of heterogeneous fog. Finally, we also propose a model allowing to evaluate the potential safety benefit of an ADAS based on the display of defogged images. read more read less

Topics:

Advanced driver assistance systems (56%)56% related to the paper, Visibility (geometry) (54%)54% related to the paper, Local algorithm (53%)53% related to the paper
View PDF
376 Citations
open accessOpen access Journal Article DOI: 10.1109/MITS.2011.942779
Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety

Abstract:

The article deals with the analysis and interpretation of dynamic scenes typical of urban driving. The key objective is to assess risks of collision for the ego-vehicle. We describe our concept and methods, which we have integrated and tested on our experimental platform on a Lexus car and a driving simulator. The on-board se... The article deals with the analysis and interpretation of dynamic scenes typical of urban driving. The key objective is to assess risks of collision for the ego-vehicle. We describe our concept and methods, which we have integrated and tested on our experimental platform on a Lexus car and a driving simulator. The on-board sensors deliver visual, telemetric and inertial data for environment monitoring. The sensor fusion uses our Bayesian Occupancy Filter for a spatio-temporal grid representation of the traffic scene. The underlying probabilistic approach is capable of dealing with uncertainties when modeling the environment as well as detecting and tracking dynamic objects. The collision risks are estimated as stochastic variables and are predicted for a short period ahead with the use of Hidden Markov Models and Gaussian processes. The software implementation takes advantage of our methods, which allow for parallel computation. Our tests have proven the relevance and feasibility of our approach for improving the safety of car driving. read more read less

Topics:

Driving simulator (56%)56% related to the paper, Probabilistic logic (53%)53% related to the paper, Probabilistic analysis of algorithms (52%)52% related to the paper, Sensor fusion (51%)51% related to the paper, Hidden Markov model (51%)51% related to the paper
View PDF
316 Citations
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Frequently asked questions

1. Can I write IEEE Intelligent Transportation Systems Magazine in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the IEEE Intelligent Transportation Systems Magazine guidelines and auto format it.

2. Do you follow the IEEE Intelligent Transportation Systems Magazine guidelines?

Yes, the template is compliant with the IEEE Intelligent Transportation Systems Magazine guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in IEEE Intelligent Transportation Systems Magazine?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the IEEE Intelligent Transportation Systems Magazine citation style.

4. Can I use the IEEE Intelligent Transportation Systems Magazine templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for IEEE Intelligent Transportation Systems Magazine.

5. Can I use a manuscript in IEEE Intelligent Transportation Systems Magazine that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper IEEE Intelligent Transportation Systems Magazine that you can download at the end.

6. How long does it usually take you to format my papers in IEEE Intelligent Transportation Systems Magazine?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in IEEE Intelligent Transportation Systems Magazine.

7. Where can I find the template for the IEEE Intelligent Transportation Systems Magazine?

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8. Can I reformat my paper to fit the IEEE Intelligent Transportation Systems Magazine's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. IEEE Intelligent Transportation Systems Magazine an online tool or is there a desktop version?

SciSpace's IEEE Intelligent Transportation Systems Magazine is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like IEEE Intelligent Transportation Systems Magazine?

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After writing your paper autoformatting in IEEE Intelligent Transportation Systems Magazine, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is IEEE Intelligent Transportation Systems Magazine's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for IEEE Intelligent Transportation Systems Magazine?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for IEEE Intelligent Transportation Systems Magazine. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In IEEE Intelligent Transportation Systems Magazine?

The 5 most common citation types in order of usage for IEEE Intelligent Transportation Systems Magazine are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the IEEE Intelligent Transportation Systems Magazine?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per IEEE Intelligent Transportation Systems Magazine's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download IEEE Intelligent Transportation Systems Magazine in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in IEEE Intelligent Transportation Systems Magazine Endnote style according to Elsevier guidelines.

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