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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: closed (30 April 2023) | Viewed by 14873

Special Issue Editors


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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

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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 2400 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 (9 papers)

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Research

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23 pages, 609 KiB  
Article
An Optimization Framework for Information Management in Adaptive Automotive Human–Machine Interfaces
by Francesco Tufano, Sushant Waman Bahadure, Manuela Tufo, Luigi Novella, Giovanni Fiengo and Stefania Santini
Appl. Sci. 2023, 13(19), 10687; https://doi.org/10.3390/app131910687 - 26 Sep 2023
Cited by 1 | Viewed by 997
Abstract
In recent years, advancements in Intelligent and Connected Vehicles (ICVs) have led to a significant increase in the amount of information to the driver through Human–Machine Interfaces (HMIs). To prevent driver cognitive overload, the development of Adaptive HMIs (A-HMIs) has emerged. Indeed, A-HMIs [...] Read more.
In recent years, advancements in Intelligent and Connected Vehicles (ICVs) have led to a significant increase in the amount of information to the driver through Human–Machine Interfaces (HMIs). To prevent driver cognitive overload, the development of Adaptive HMIs (A-HMIs) has emerged. Indeed, A-HMIs regulate information flows by dynamically adapting the presentation to suit the contextual driving conditions. This paper presents a novel methodology, based on multi-objective optimization, that offers a more generalized design approach for adaptive strategies in A-HMIs. The proposed methodology is specifically tailored for designing an A-HMI that, by continuously monitoring the Driver–Vehicle–Environment (DVE) system, schedules actions requested by applications and selects appropriate presentation modalities to suit the current state of the DVE. The problem to derive these adaptive strategies is formulated as an optimization task where the objective is to find a set of rules to manage information flow between vehicle and driver that minimizes both the driver’s workload and the queuing of actions. To achieve these goals, the methodology evaluates through two indexes how applications’ requests impact the driver’s cognitive load and the waiting queue for actions. The optimization procedure has been solved offline to define adaptive strategies for scheduling five application requests, i.e., forward collision warning, system interaction, turn indicators, infotainment volume increase, and phone calls. A theoretical analysis has demonstrated the effectiveness of the proposed framework in optimizing the prioritization strategy for actions requested by applications. By adopting this approach, the design of rules for the scheduling process of the A-HMI architecture is significantly streamlined while gaining adaptive capabilities to prevent driver cognitive overload. Full article
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23 pages, 7955 KiB  
Article
Platooning Cooperative Adaptive Cruise Control for Dynamic Performance and Energy Saving: A Comparative Study of Linear Quadratic and Reinforcement Learning-Based Controllers
by Angelo Borneo, Luca Zerbato, Federico Miretti, Antonio Tota, Enrico Galvagno and Daniela Anna Misul
Appl. Sci. 2023, 13(18), 10459; https://doi.org/10.3390/app131810459 - 19 Sep 2023
Cited by 4 | Viewed by 1047
Abstract
In recent decades, the automotive industry has moved towards the development of advanced driver assistance systems to enhance the comfort, safety, and energy saving of road vehicles. The increasing connection and communication between vehicles (V2V) and infrastructure (V2I) enables further opportunities for their [...] Read more.
In recent decades, the automotive industry has moved towards the development of advanced driver assistance systems to enhance the comfort, safety, and energy saving of road vehicles. The increasing connection and communication between vehicles (V2V) and infrastructure (V2I) enables further opportunities for their optimisation and allows for additional features. Among others, vehicle platooning is the coordinated control of a set of vehicles moving at a short distance, one behind the other, to minimise aerodynamic losses, and it represents a viable solution to reduce the energy consumption of freight transport. To achieve this aim, a new generation of adaptive cruise control is required, namely, cooperative adaptive cruise control (CACC). The present work aims to compare two CACC controllers applied to a platoon of heavy-duty electric trucks sharing the same linear spacing policy. A control technique based on reinforcement learning (RL) algorithm, with a deep deterministic policy gradient, and a classic linear quadratic control (LQC) are investigated. The comparative analysis of the two controllers evaluates the ability to track inter-vehicle distance and vehicle speed references during a standard driving cycle, the string stability, and the transient response when an unexpected obstacle occurs. Several performance indices (i.e., acceleration and jerk, battery state of charge, and energy consumption) are introduced as metrics to highlight the differences. By appropriately selecting the reward function of the RL algorithm, the analysed controllers achieve similar goals in terms of platoon dynamics, energy consumption, and string stability. Full article
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11 pages, 1228 KiB  
Article
A Personal Microcomputer as an Access Control Management Platform in Road Transport
by Przemysław Wójcik and Tomasz Neumann
Appl. Sci. 2023, 13(17), 9770; https://doi.org/10.3390/app13179770 - 29 Aug 2023
Viewed by 845
Abstract
For many years, the use of new computer systems to control various elements of everyday human life has been observed. Separate systems manage access control; others are used to control blinds and roller shutters; and others manage systems in the garden. Many of [...] Read more.
For many years, the use of new computer systems to control various elements of everyday human life has been observed. Separate systems manage access control; others are used to control blinds and roller shutters; and others manage systems in the garden. Many of these systems can be integrated using available systems. This paper presents an example of an access control management system based on the Raspberry Pi microcomputer and shows an analysis of its performance, accuracy, and possibility of improvement. This study used official devices manufactured by the Raspberry Pi Foundation; however, it is possible to create a similar system with custom parts. This project used open-source software. The authors argued that it is possible to create an autonomous vehicle access control system using microcomputers and optical character recognition technology. Using simple devices, the plate recognition system was built and tested, proving the thesis that it is possible to build an access control system using available devices. This also confirms the thesis that microcomputers can be used to control other systems in the human environment. Full article
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17 pages, 9293 KiB  
Article
Modular Bus Unit Scheduling for an Autonomous Transit System under Range and Charging Constraints
by Hong Gao, Kai Liu, Jiangbo Wang and Fangce Guo
Appl. Sci. 2023, 13(13), 7661; https://doi.org/10.3390/app13137661 - 28 Jun 2023
Cited by 4 | Viewed by 1331
Abstract
Recent advances in vehicle technology offer new opportunities for an electric, automated, modular bus (MB) unit with an adjustable capacity to be applied to transit systems, promising to tackle the resource allocation challenges of traditional buses in coping with uneven travel demand. Drawing [...] Read more.
Recent advances in vehicle technology offer new opportunities for an electric, automated, modular bus (MB) unit with an adjustable capacity to be applied to transit systems, promising to tackle the resource allocation challenges of traditional buses in coping with uneven travel demand. Drawing on the concept of modular vehicles, this paper introduces a novel scheduling system in which MB units can be combined/separated from fulfilling imbalanced trip demands through capacity adjustments. We develop an optimization model for determining the optimal formation and trip sequence of MB units. In particular, given that the vehicles are electrically powered, battery range limits and charging plans are considered in the system scheduling process. A column-generation-based heuristic algorithm is designed to efficiently solve this model, with constraints related to travel demand and charging station capacity incorporated into the master problem and the trip sequence for modular units with limited energy solved by the subproblem. Taking real data from transit operations for numerical examples, the proposed model performs well in terms of both algorithmic performance and practical applications. The generated optimal MB dispatching scheme can significantly reduce the operating cost from $1534.31 to $1144.26, a decrease of approximately 25% compared to conventional electric buses. The sensitivity analysis on the MB dispatch cost and battery capacity provides some insights for both the scenario configuration and the battery selection for MB system implementation. Full article
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22 pages, 2399 KiB  
Article
Electrical Circuits Simulator in Null-Flux Electrodynamic Suspension Analysis
by Thaís N. França, Hengda Li, Hanlin Zhu, Hongfu Shi, Le Liang and Zigang Deng
Appl. Sci. 2023, 13(11), 6666; https://doi.org/10.3390/app13116666 - 30 May 2023
Viewed by 1143
Abstract
This paper employed an electrical circuit simulator to investigate an electrodynamic suspension system (EDS) for passenger rail transport applications. Focusing on a null-flux suspension system utilizing figure-eight-shaped coils (8-shaped coils), the aim was to characterize the three primary electromagnetic forces generated in an [...] Read more.
This paper employed an electrical circuit simulator to investigate an electrodynamic suspension system (EDS) for passenger rail transport applications. Focusing on a null-flux suspension system utilizing figure-eight-shaped coils (8-shaped coils), the aim was to characterize the three primary electromagnetic forces generated in an EDS and to compare the findings with existing literatures. The dynamic circuit theory (DCT) approach was utilized to model the system as an electrical circuit with lumped parameters, and mutual inductance values between the superconducting (SC) coil and the upper and lower loops of the 8-shaped coil were calculated and inputted into the simulator. The results were compared with experimental data obtained from the Yamanashi test track. The comparison demonstrated close alignment between the theoretical expectations and the obtained experimental curves, validating the accuracy of the proposed model. The study highlights the advantages of this new approach, including faster computation times and efficient implementation of modifications. Overall, this work contributes to the ongoing development and optimization of null-flux suspension Maglev systems. Full article
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16 pages, 1450 KiB  
Article
A Deep Reinforcement Learning Approach for Efficient, Safe and Comfortable Driving
by Dinesh Cyril Selvaraj, Shailesh Hegde, Nicola Amati, Francesco Deflorio and Carla Fabiana Chiasserini
Appl. Sci. 2023, 13(9), 5272; https://doi.org/10.3390/app13095272 - 23 Apr 2023
Cited by 7 | Viewed by 1793
Abstract
Sensing, computing, and communication advancements allow vehicles to generate and collect massive amounts of data on their state and surroundings. Such richness of information fosters data-driven decision-making model development that considers the vehicle’s environmental context. We propose a data-centric application of Adaptive Cruise [...] Read more.
Sensing, computing, and communication advancements allow vehicles to generate and collect massive amounts of data on their state and surroundings. Such richness of information fosters data-driven decision-making model development that considers the vehicle’s environmental context. We propose a data-centric application of Adaptive Cruise Control employing Deep Reinforcement Learning (DRL). Our DRL approach considers multiple objectives, including safety, passengers’ comfort, and efficient road capacity usage. We compare the proposed framework’s performance to traditional ACC approaches by incorporating such schemes into the CoMoVe framework, which realistically models communication, traffic, and vehicle dynamics. Our solution offers excellent performance concerning stability, comfort, and efficient traffic flow in diverse real-world driving conditions. Notably, our DRL scheme can meet the desired values of road usage efficiency most of the time during the lead vehicle’s speed-variation phases, with less than 40% surpassing the desirable headway. In contrast, its alternatives increase headway during such transient phases, exceeding the desired range 85% of the time, thus degrading performance by over 300% and potentially contributing to traffic instability. Furthermore, our results emphasize the importance of vehicle connectivity in collecting more data to enhance the ACC’s performance. Full article
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17 pages, 14543 KiB  
Article
A Lightweight Network for Real-Time Rain Streaks and Rain Accumulation Removal from Single Images Captured by AVs
by Esraa Khatab, Ahmed Onsy, Martin Varley and Ahmed Abouelfarag
Appl. Sci. 2023, 13(1), 219; https://doi.org/10.3390/app13010219 - 24 Dec 2022
Cited by 3 | Viewed by 1443
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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16 pages, 3680 KiB  
Article
Comprehensive Automated Driving Maneuvers under a Non-Signalized Intersection Adopting Deep Reinforcement Learning
by Quang-Duy Tran and Sang-Hoon Bae
Appl. Sci. 2022, 12(19), 9653; https://doi.org/10.3390/app12199653 - 26 Sep 2022
Cited by 1 | Viewed by 1362
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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Review

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29 pages, 2391 KiB  
Review
Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey
by Hanan Rizk, Ahmed Chaibet and Ali Kribèche
Appl. Sci. 2023, 13(11), 6700; https://doi.org/10.3390/app13116700 - 31 May 2023
Cited by 3 | Viewed by 3743
Abstract
Autonomous driving has the potential to revolutionize mobility and transportation by reducing road accidents, alleviating traffic congestion, and mitigating air pollution. This transformation can result in energy efficiency, enhanced convenience, and increased productivity, as valuable driving time can be repurposed for other activities. [...] Read more.
Autonomous driving has the potential to revolutionize mobility and transportation by reducing road accidents, alleviating traffic congestion, and mitigating air pollution. This transformation can result in energy efficiency, enhanced convenience, and increased productivity, as valuable driving time can be repurposed for other activities. The main objective of this paper is to provide a comprehensive technical survey of the latest research in the field of lateral, longitudinal, and integrated control techniques for autonomous vehicles. The survey aims to explore a wide range of techniques and methodologies employed to achieve precise steering control while also considering longitudinal aspects. Model-based control techniques form the foundation for control, utilizing mathematical models of vehicle dynamics to design controllers that effectively track desired speeds and/or steering behavior. Unlike model-free control techniques such as reinforcement learning and deep learning algorithms facilitate the integration of longitudinal and lateral control by learning control policies directly from data and without explicit knowledge of the underlying dynamics. Through this survey, the paper delves into the strengths, limitations, and advancements in both model-based and model-free control approaches for autonomous vehicles. It investigates their performance in real-world scenarios and addresses the technical challenges associated with their implementation. These challenges may include uncertainties in the environment, adaptability to dynamic conditions, robustness, safety considerations, and computational complexity. Full article
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