The New Devices to Assist the Driver (ADAS)

A special issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: closed (25 March 2021) | Viewed by 4253

Special Issue Editors

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Guest Editor
Department of Naval and Industrial Engineering, University of La Coruña, 15403 Ferrol, Spain
Interests: multibody system dynamics; vehicle dynamics; applications to the automotive sector
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Instituto Universitario de Investigación del Automóvil (INSIA), Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: intelligent transport systems; advanced driver assistance systems; vehicle positioning; inertial sensors; digital maps; vehicle dynamics; driver monitoring; perception; autonomous vehicles; cooperative services; connected and autonomous driving
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The vehicle industry has recently witnessed a surge in experimentation with automation, with the advanced driving assistance systems currently being used continually evolving to more complex and efficient systems. These new assistance systems and the applications of autonomous driving of road vehicles imply that there will be ever greater requirements for lower-level systems: perception systems that are robust enough to avoid false positives or false negatives when making decisions, actuation systems which are quick and effective, communication systems that receive information from the road and adjacent cars, operative systems that are fast and have a low energy consumption, etc. Additionally, the integration of all these systems leads to different architectures and the use of different hardware (ECUs, FPGAs, etc.), providing a wide set of solutions—and many of these systems rely on the employment of more or less complex vehicle models in order to obtain virtual sensors.

A deep comprehension of the different solutions and the current state-of-the-art of these systems is needed to contribute to the advance of the systems, increasing the security levels of the vehicles. In all areas, it is crucial to study the limitations of each of the solutions, as well as to establish tools that try to alleviate these issues, through improvements in either hardware or software.

This Special Issue aims to provide innovative developments in areas related to driving assistance and analytical reviews of the state-of-the-art of the involved systems.

Authors are invited to contact the guest editors prior to submission if they are uncertain as to whether their work falls within the general scope of this Special Issue.

Prof. Dr. Miguel Ángel Naya Villaverde
Prof. Dr. Felipe Jiménez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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. Vehicles is an international peer-reviewed open access quarterly 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 1600 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.


  • Road vehicles
  • Driver assistance systems
  • Braking assistance
  • Traction assistance
  • Stability systems
  • Sensors
  • Virtual sensors
  • Vehicle surroundings surveillance
  • Sensor fusion
  • Positioning
  • Computer vision
  • Autonomous vehicles
  • Vehicle tracking

Published Papers (1 paper)

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16 pages, 987 KiB  
Comparative Analysis of Machine Learning-Based Approaches for Anomaly Detection in Vehicular Data
by Konstantinos Demestichas, Theodoros Alexakis, Nikolaos Peppes and Evgenia Adamopoulou
Vehicles 2021, 3(2), 171-186; - 25 Apr 2021
Cited by 9 | Viewed by 3366
The rapid growth of demand for transportation, both for people and goods, as well as the massive accumulation of population in urban centers has augmented the need for the development of smart transport systems. One of the needs that have arisen is to [...] Read more.
The rapid growth of demand for transportation, both for people and goods, as well as the massive accumulation of population in urban centers has augmented the need for the development of smart transport systems. One of the needs that have arisen is to efficiently monitor and evaluate driving behavior, so as to increase safety, provide alarms, and avoid accidents. Capitalizing on the evolution of Information and Communication Technologies (ICT), the development of intelligent vehicles and platforms in this domain is getting more feasible than ever. Nowadays, vehicles, as well as highways, are equipped with sensors that collect a variety of data, such as speed, acceleration, fuel consumption, direction, and more. The methodology presented in this paper combines both advanced machine learning algorithms and open-source based tools to correlate different data flows originating from vehicles. Particularly, the data gathered from different vehicles are processed and analyzed with the utilization of machine learning techniques in order to detect abnormalities in driving behavior. Results from different suitable techniques are presented and compared, using an extensive real-world dataset containing field measurements. The results feature the application of both supervised univariate anomaly detection and unsupervised multivariate anomaly detection methods in the same dataset. Full article
(This article belongs to the Special Issue The New Devices to Assist the Driver (ADAS))
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