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Applied Sciences
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Vehicle Technology and Its Applications
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Vehicle Technology and Its Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 29 August 2024 | Viewed by 4430

Special Issue Editors

Dr. Mauro Dell’Orco

E-Mail Website
Guest Editor
DICATECh, Polytechnic University of Bari, Bari, Italy
Interests: transport planning with AI techniques like neural networks, fuzzy logic, and evidence theory in intelligent transport systems; operational research to optimize transport models; logistics
Dr. Mario Marinelli

E-Mail Website
Guest Editor
DICATECh, Polytechnic University of Bari, Bari, Italy
Interests: intelligent transportation systems; artificial intelligence techniques for transportation planning; optimization and operations research models for transportation and logistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The focus of this Special Issue is to highlight recent developments in vehicle technology and its applications. Rapid advancements in technology have brought about significant transformative changes in the automotive industry. Vehicle technology encompasses various aspects such as electrification, autonomous driving, connectivity, and advanced driver assistance systems (ADASs). These innovations have significant implications for safety, sustainability, and user experience.

The integration of artificial intelligence (AI) and machine learning has fueled breakthroughs in autonomous driving. AI-powered algorithms enable vehicles to perceive their surroundings, make real-time decisions, and navigate complex scenarios. Furthermore, vehicle connectivity facilitates communication between vehicles, infrastructure, and the surrounding environment, enabling features like vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication.

Electric vehicle technology has also gained momentum, addressing concerns about environmental impact and fossil fuel dependence. Battery advancements, charging infrastructure expansion, and energy management systems have enhanced electric vehicles' (EVs) feasibility and desirability.

As the automotive landscape evolves, this Special Issue welcomes the submission of original research and comprehensive reviews focusing on the latest advancements in vehicle technology and its diverse applications.

We invite submissions that explore topics including, but not limited to:

  • Autonomous vehicle algorithms and perception systems;
  • Human–machine interfaces and user experience in vehicles;
  • Energy-efficient propulsion systems and powertrain technologies;
  • Connectivity solutions and in-car infotainment systems;
  • Safety enhancements through ADAS and predictive analytics;
  • Environmental impact and sustainability of vehicle technologies;
  • Electric vehicle charging infrastructure and smart grid integration.

Join us in shedding light on the innovative strides being made in the field of vehicle technology and its broad-reaching impact.

Sincerely,

Dr. Mauro Dell’Orco
Dr. Mario Marinelli
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

  • vehicle technology
  • autonomous driving
  • advanced driver assistance systems (ADASs)
  • electric vehicles (EVs)
  • connectivity
  • artificial intelligence (AI)
  • machine learning
  • vehicle-to-vehicle (V2V) communication
  • vehicle-to-infrastructure (V2I) communication
  • sustainability

Published Papers (5 papers)

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Research

17 pages, 7700 KiB  
Article
Proactive Braking Control System for Collision Avoidance during Right Turns with Occluded Vision at an Intersection
by Sota Aoki, Yohei Fujinami, Pongsathorn Raksincharoensak and Roman Henze
Appl. Sci. 2024, 14(6), 2661; https://doi.org/10.3390/app14062661 - 21 Mar 2024
Viewed by 464
Abstract
This paper describes the development of an Advanced Driver Assistance System (ADAS) which will allow drivers to avoid collisions with an oncoming vehicle from an occluded area when turning right at intersections in left-hand traffic. Connected vehicles, in coordination with infrastructure, represent one [...] Read more.
This paper describes the development of an Advanced Driver Assistance System (ADAS) which will allow drivers to avoid collisions with an oncoming vehicle from an occluded area when turning right at intersections in left-hand traffic. Connected vehicles, in coordination with infrastructure, represent one of the commercialized ADAS technologies for collision avoidance. However, the coverage of the ADAS will be limited to designated intersections only, as communication equipment needs to be installed in both the vehicle and infrastructure to enable the assistance. This paper proposes an ADAS using on-board sensors, independent of infrastructure facilities, to control the vehicle velocity to avoid collisions. Most current intersection assistance, by using an Autonomous Emergency Braking System (AEBS), allows the driver to avoid a collision with oncoming vehicles when there is clear vision without occlusion. However, many accidents occur when the vehicle detects the oncoming vehicle too late because of occlusion in the intersection, such as a vehicle in the opposite lane. This system calculates the hazardous speed criteria of the ego vehicle, which might result in a high risk of collision when darting out occurs, and provides speed control assistance to allow the driver to escape from the hazardous speed region. The simulation results reveal that the proposed system effectively reduces the possibility of collisions compared to conventional AEBS. Full article
(This article belongs to the Special Issue Vehicle Technology and Its Applications)
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15 pages, 5619 KiB  
Communication
Enhanced Vehicle Dynamics and Safety through Tire–Road Friction Estimation for Predictive ELSD Control under Various Conditions of General Racing Tracks
by Seunghoon Woo, Seunguk Jeon, Eunhyek Joa and Donghoon Shin
Appl. Sci. 2024, 14(5), 1903; https://doi.org/10.3390/app14051903 - 26 Feb 2024
Viewed by 1116
Abstract
This study focuses on the tire–road friction estimation for the predictive control strategy of electronically limited slip differential (ELSD) to improve the handling and acceleration performance of front-wheel drive cars, which typically suffer from excessive understeer and inner drive wheel spin during acceleration [...] Read more.
This study focuses on the tire–road friction estimation for the predictive control strategy of electronically limited slip differential (ELSD) to improve the handling and acceleration performance of front-wheel drive cars, which typically suffer from excessive understeer and inner drive wheel spin during acceleration while turning due to reduced vertical load on the wheel. To mitigate this, we propose a control logic for ELSD that enhances course followability and acceleration by pre-transferring the driving torque from the inside to the outside wheel, considering the estimated traction potential for rapid response. It is essential to improve the control accuracy of wheel spin prediction by predicting the friction coefficient of the road surface. Furthermore, this study extends to the analysis of vehicle dynamics during lane-change maneuvers on low-friction surfaces, emphasizing the role of accurate tire–road friction estimation in vehicle safety. A CarSim 2023-based simulation study was conducted to investigate the vehicle response on snowy roads with low friction coefficients (μ = 0.2) and low temperatures (−5 °C). The results demonstrated that even minimal steering input could result in significant side-slip angles, highlighting the nonlinear vehicle behavior and the critical need for robust traction estimation in such challenging conditions of general racing tracks. The proposed friction-estimation method was evaluated through vehicle testing and has been substantiated by patents for its originality in control and friction-estimation approaches. The outcomes of these combined methodologies underline the critical importance of tire–road friction coefficient estimation in both the effectiveness of the ELSD system and the broader context of active safety systems. Full article
(This article belongs to the Special Issue Vehicle Technology and Its Applications)
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25 pages, 1179 KiB  
Article
Four-Wheeled Vehicle Sideslip Angle Estimation: A Machine Learning-Based Technique for Real-Time Virtual Sensor Development
by Guido Napolitano Dell’Annunziata, Marco Ruffini, Raffaele Stefanelli, Giovanni Adiletta, Gabriele Fichera and Francesco Timpone
Appl. Sci. 2024, 14(3), 1036; https://doi.org/10.3390/app14031036 - 25 Jan 2024
Viewed by 973
Abstract
In the last few decades, the role of vehicle dynamics control systems has become crucial. In this complex scenario, the correct real-time estimation of the vehicle’s sideslip angle is decisive. Indeed, this quantity is deeply linked to several aspects, such as traction and [...] Read more.
In the last few decades, the role of vehicle dynamics control systems has become crucial. In this complex scenario, the correct real-time estimation of the vehicle’s sideslip angle is decisive. Indeed, this quantity is deeply linked to several aspects, such as traction and stability optimization, and its correct understanding leads to the possibility of reaching greater road safety, increased efficiency, and a better driving experience for both autonomous and human-controlled vehicles. This paper aims to estimate accurately the sideslip angle of the vehicle using different neural network configurations. Then, the proposed approach involves using two separate neural networks in a dual-network architecture. The first network is dedicated to estimating the longitudinal velocity, while the second network predicts the sideslip angle and takes the longitudinal velocity estimate from the first network as input. This enables the creation of a virtual sensor to replace the real one. To obtain a reliable training dataset, several test sessions were conducted on different tracks with various layouts and characteristics, using the same reference instrumented vehicle. Starting from the acquired channels, such as lateral and longitudinal acceleration, steering angle, yaw rate, and angular wheel speeds, it has been possible to estimate the sideslip angle through different neural network architectures and training strategies. The goodness of the approach was assessed by comparing the estimations with the measurements obtained from an optical sensor able to provide accurate values of the target variable. The obtained results show a robust alignment with the reference values in a great number of tested conditions. This confirms that the adoption of artificial neural networks represents a reliable strategy to develop real-time virtual sensors for onboard solutions, expanding the information available for controls. Full article
(This article belongs to the Special Issue Vehicle Technology and Its Applications)
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15 pages, 2715 KiB  
Article
An Intelligent Chinese Driver Road Performance Assessment Model (RPAM) for Future Licensing Examinations
by Jianguo Gong, Boao Zhang, Yibing Liu, Jiayi Lu, Yuan Ma and Yaoguang Cao
Appl. Sci. 2023, 13(24), 13066; https://doi.org/10.3390/app132413066 - 07 Dec 2023
Viewed by 596
Abstract
As the demand for private vehicles rises, there has been a gradual increase in the number of motor vehicles on the roads, leading to a growing concern about addressing traffic safety. Currently, China’s approach to assessing driver capabilities remains rooted in traditional, non-intelligent, [...] Read more.
As the demand for private vehicles rises, there has been a gradual increase in the number of motor vehicles on the roads, leading to a growing concern about addressing traffic safety. Currently, China’s approach to assessing driver capabilities remains rooted in traditional, non-intelligent, and standardized evaluation methods based on examination subjects. The traditional model often falls short in providing constructive feedback on a driver’s real-world vehicle handling abilities, as many of the examination subjects can be practiced in advance to achieve a mere passing result, which, undoubtedly, increases the likelihood of underqualified drivers on the road. To address the issues of the current examination-oriented driver evaluation system in China, we propose a road performance assessment model (RPAM) that assesses drivers comprehensively by evaluating their road environment perception and vehicle operation abilities based on an in-vehicle and out-vehicle perception system. The model leverages patterns of the driver’s head posture, along with real-time information on the vehicle’s behavior and the road conditions, to quantify various performance metrics related to reasonable operation processes. These metrics are then integrated to generate a holistic assessment of the driving capabilities. This paper ultimately conducted tests of the RPAM on one actual examination route in Beijing. Two drivers were randomly selected for the examination. The model successfully computed the overall ability scores for each driver, validating the effectiveness. Full article
(This article belongs to the Special Issue Vehicle Technology and Its Applications)
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23 pages, 7941 KiB  
Article
Conceptual Design and Energy Efficiency Evaluation for a Novel Torque Vectoring Differential Applied to Front-Wheel-Drive Electric Vehicles
by Cheng-Ta Chung and Hung-Yih Tsai
Appl. Sci. 2023, 13(20), 11434; https://doi.org/10.3390/app132011434 - 18 Oct 2023
Viewed by 714
Abstract
This paper presents a novel differential with lateral torque vectoring activated by an auxiliary motor, called motor-modulated lateral torque vectoring differential, or briefly, MLTD. Its architecture and kinematic characteristics are described, and the optimal cornering performance due to torque vectoring is evaluated using [...] Read more.
This paper presents a novel differential with lateral torque vectoring activated by an auxiliary motor, called motor-modulated lateral torque vectoring differential, or briefly, MLTD. Its architecture and kinematic characteristics are described, and the optimal cornering performance due to torque vectoring is evaluated using a steady-state vehicle dynamic model. Then, a conceptual design case of MLTD installed in a front-wheel-drive electric vehicle is conducted to assess its feasibility in terms of cornering, driving performances, and energy efficiency performance. The calculations show that an optimal distribution ratio between left and right torques for maximum lateral acceleration can be obtained for a specific cornering condition, further used for DM sizing in the preliminary stage. The simulations for energy consumption at constant-speed turning reveal that energy efficiencies of MLTD are lower than those of conventional differentials with evenly distributed torques. However, this deficiency may be paid off by moderately cutting down the rolling resistance of tire while the superior cornering performances brought by torque vectoring is still preserved. Accordingly, the newly proposed MLTD possesses greater flexibility to improve energy efficiency and driving range of electric vehicles without trading off against its desirable cornering performance and safe handling, and is thus worthy of further development. Full article
(This article belongs to the Special Issue Vehicle Technology and Its Applications)
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