Special Issue "Driving Automation Systems and Connectivity for a Sustainable Mobility"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 30 April 2023 | Viewed by 1351

Special Issue Editors

Prof. Daniela Anna Misul
E-Mail Website
Guest Editor
Department of Energy, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Interests: internal combustion engines; electrified powertrains; artificial intelligence for sustainable mobility; ADAS
Special Issues, Collections and Topics in MDPI journals
Dr. Gabriele Di Blasio
E-Mail Website
Guest Editor
STEMS - CNR, via Marconi, 4, 80125 Napoli, Italy
Interests: electrified powertrains; technologies for sustainable mobility; renewable fuels; ADAS
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Alfredo Gimelli
E-Mail Website1 Website2
Guest Editor
Department of Industrial Engineering, University of Naples Federico II, 80100 Naples, Italy
Interests: multi sourses hybrid polygeneration energy convertion systems; internal combustion engines; electrified powertrains; technologies for sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The worldwide regulations for emissions and carbon footprint reduction, together with the shortage in fossil fuels, are constantly driving the industry and researchers to explore advanced solutions for the cleaner exploitation of energy. Therefore, automotive sectors are seeking new solutions in the transport sector and have made large investments in the electrification of existing powertrains, together with the exploitation of ICT facilities, for more intelligent and more efficient methods of mobility. Within this scenario, advanced driver-assistance systems (ADAS) were initially conceived to achieve improved driving comfort and prevent human error, thus reducing road casualties.

Still, the potential of ADAS for energy saving is currently at the forefront of scientific research and is gaining increasing interest. More specifically, the exploitation of the full potential of electrified vehicles (xEV) requires optimum control to cope with the constraints of certain driving journeys and driving behavior. The latter issues could benefit from the prediction of future road conditions, which could be provided by connected and autonomous vehicles (CAVs) Moreover, specific control algorithms could allow for an efficient exploitation of on-board stored energy through optimal driving control and traffic information management. Finally, ICT security issues should also be thoroughly considered for smart control solutions.

This Special Issue encourages researchers working in this field to share their latest finidngs for advanced driver-assistance systems (ADAS) and connected and autonomous vehicles (CAVs) to improve a sustainable mobility, e.g., experimental tests, control strategies, and new layouts of CAVs.

Specific topics of interest for publication include but are not limited to:

  • Theory, methods, models, optimization and control of xEVs equipped with driving automation systems;
  • Sensors, data processing and management of xEVs and CAVs;
  • Scenario definition, testing and validation (xIL, etc.) of DASs;
  • Assessment of CAVs environmental impact and energy savings;
  • Advanced control strategies (MPC, AI, etc.) for driving automation system applications;
  • Eco-driving solutions for xEVs and CAVs;
  • Trajectory planning and vehicle control from the perspective of energy consumption, driveability and/or battery lifetime;
  • Dedicated solutions for cooperative adaptive cruise control (CACC) and platooning.

Dr. Daniela Anna Misul
Dr. Gabriele Di Blasio
Prof. Dr. Alfredo Gimelli
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. Applied Sciences 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 2300 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

  • advanced driver-assistance systems
  • connected and autonomous vehicles
  • sensors, data management, driving automation levels
  • eco-driving
  • platooning
  • connected and automated mobility
  • cooperative adaptive cruise control

Published Papers (2 papers)

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Research

Article
A Lightweight Network for Real-Time Rain Streaks and Rain Accumulation Removal from Single Images Captured by AVs
Appl. Sci. 2023, 13(1), 219; https://doi.org/10.3390/app13010219 - 24 Dec 2022
Viewed by 358
Abstract
In autonomous driving, object detection is considered a base step to many subsequent processes. However, object detection is challenged by loss in visibility caused by rain. Rainfall occurs in two main forms, which are streaks and streaks accumulations. Each degradation type imposes different [...] Read more.
In autonomous driving, object detection is considered a base step to many subsequent processes. However, object detection is challenged by loss in visibility caused by rain. Rainfall occurs in two main forms, which are streaks and streaks accumulations. Each degradation type imposes different effect on the captured videos; therefore, they cannot be mitigated in the same way. We propose a lightweight network which mitigates both types of rain degradation in real-time, without negatively affecting the object-detection task. The proposed network consists of two different modules which are used progressively. The first one is a progressive ResNet for rain streaks removal, while the second one is a transmission-guided lightweight network for rain streak accumulation removal. The network has been tested on synthetic and real rainy datasets and has been compared with state-of-the-art (SOTA) networks. Additionally, time performance evaluation has been performed to ensure real-time performance. Finally, the effect of the developed deraining network has been tested on YOLO object-detection network. The proposed network exceeded SOTA by 1.12 dB in PSNR on the average result of multiple synthetic datasets with 2.29× speedup. Finally, it can be observed that the inclusion of different lightweight stages works favorably for real-time applications and could be updated to mitigate different degradation factors such as snow and sun blare. Full article
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Article
Comprehensive Automated Driving Maneuvers under a Non-Signalized Intersection Adopting Deep Reinforcement Learning
Appl. Sci. 2022, 12(19), 9653; https://doi.org/10.3390/app12199653 - 26 Sep 2022
Cited by 1 | Viewed by 530
Abstract
Automated driving systems have become a potential approach to mitigating collisions, emissions, and human errors in mixed-traffic environments. This study proposes the use of a deep reinforcement learning method to verify the effects of comprehensive automated vehicle movements at a non-signalized intersection according [...] Read more.
Automated driving systems have become a potential approach to mitigating collisions, emissions, and human errors in mixed-traffic environments. This study proposes the use of a deep reinforcement learning method to verify the effects of comprehensive automated vehicle movements at a non-signalized intersection according to training policy and measures of effectiveness. This method integrates multilayer perceptron and partially observable Markov decision process algorithms to generate a proper decision-making algorithm for automated vehicles. This study also evaluates the efficiency of proximal policy optimization hyperparameters for the performance of the training process. Firstly, we set initial parameters and create simulation scenarios. Secondly, the SUMO simulator executes and exports observations. Thirdly, the Flow tool transfers these observations into the states of reinforcement learning agents. Next, the multilayer perceptron algorithm trains the input data and updates policies to generate the proper actions. Finally, this training checks the termination and iteration process. These proposed experiments not only increase the speeds of vehicles but also decrease the emissions at a higher market penetration rate and a lower traffic volume. We demonstrate that the fully autonomous condition increased the average speed 1.49 times compared to the entirely human-driven experiment. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

1. Angelo Borneo; Enrico Galvagno; Daniela Misul, etc.  ”A reinforcement learning algorithm for dynamic performance and energy saving in a platooning cooperative ACC”

 

 

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