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Vehicle Autonomy, Safety, and Security via Mobile Crowdsensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 1660

Special Issue Editors


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Guest Editor
Korea Aerospace Research Institute, Daejeon 34133, Korea
Interests: localization; positioning; optimization; estimation; tracking; signal processing; networking; model mismatch mitigation; interference cancellation and mitigation; standardization
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Navigation R&D Division, Korea Aerospace Research Institute, Gwahak-ro, Yuseong-gu, Daejeon, Korea
Interests: GNSS-based navigation; road anomaly detection; road surface assessment; optimization

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to a Special Issue of Sensors on “Vehicle Autonomy, Safety, and Security via Mobile Crowdsensing”. In the early 2000s, tremendous efforts were made in wireless sensor networks with the rapid increase in the need for sensing surroundings to enable system automation based on the readings from sensors. The concept of wireless sensor networks has migrated to vehicular networks with the advent of emerging vehicle technologies, particularly autonomous vehicles, where each vehicle reads its driving environment for improving not only user convenience and safety, but also network efficiency via mobile crowdsensing, which will be key to opening up new possibilities for better mobility experiences and zero fatality. However, many issues remain to be solved for practical uses.

The aim of this Special Issue is to discuss the challenges and strategies for vehicle autonomy, safety, and security with sharing up-to-date methods and applications of mobile crowdsensing. In this Special Issue, original research articles and reviews are welcome. Topics of interest include, but are not limited to:

  • System configuration and architecture design for mobile crowdsensing ;
  • Task management and incentive mechanisms;
  • Protocols and algorithms for mobile crowdsensing (routing, access control, data aggregation and deduplication, etc.);
  • Privacy, security, and data integrity (untruthful reports detection and mitigation);
  • Traffic/environment sensing and prediction (e.g., cooperative adaptive cruise control);
  • Enabling technologies for vehicle autonomy, safety, and security;
  • Applications of artificial intelligence;
  • Testbeds and testing.

Dr. Sangwoo Lee
Dr. Deok Won Lim
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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.

Published Papers (1 paper)

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Research

17 pages, 4629 KiB  
Article
Research on Aviation Safety Prediction Based on Variable Selection and LSTM
by Hang Zeng, Jiansheng Guo, Hongmei Zhang, Bo Ren and Jiangnan Wu
Sensors 2023, 23(1), 41; https://doi.org/10.3390/s23010041 - 21 Dec 2022
Cited by 4 | Viewed by 1382
Abstract
Accurate prediction of aviation safety levels is significant for the efficient early warning and prevention of incidents. However, the causal mechanism and temporal character of aviation accidents are complex and not fully understood, which increases the operation cost of accurate aviation safety prediction. [...] Read more.
Accurate prediction of aviation safety levels is significant for the efficient early warning and prevention of incidents. However, the causal mechanism and temporal character of aviation accidents are complex and not fully understood, which increases the operation cost of accurate aviation safety prediction. This paper adopts an innovative statistical method involving a least absolute shrinkage and selection operator (LASSO) and long short-term memory (LSTM). We compiled and calculated 138 monthly aviation insecure events collected from the Aviation Safety Reporting System (ASRS) and took minor accidents as the predictor. Firstly, this paper introduced the group variables and the weight matrix into LASSO to realize the adaptive variable selection. Furthermore, it took the selected variable into multistep stacked LSTM (MSSLSTM) to predict the monthly accidents in 2020. Finally, the proposed method was compared with multiple existing variable selection and prediction methods. The results demonstrate that the RMSE (root mean square error) of the MSSLSTM is reduced by 41.98%, compared with the original model; on the other hand, the key variable selected by the adaptive spare group lasso (ADSGL) can reduce the elapsed time by 42.67% (13 s). This shows that aviation safety prediction based on ADSGL and MSSLSTM can improve the prediction efficiency of the model while keeping excellent generalization ability and robustness. Full article
(This article belongs to the Special Issue Vehicle Autonomy, Safety, and Security via Mobile Crowdsensing)
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