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 2079

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
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

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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 (3 papers)

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Research

25 pages, 1179 KiB  
Article
Four-Wheeled Vehicle Sideslip Angle Estimation: A Machine Learning-Based Technique for Real-Time Virtual Sensor Development
Appl. Sci. 2024, 14(3), 1036; https://doi.org/10.3390/app14031036 - 25 Jan 2024
Viewed by 647
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
Appl. Sci. 2023, 13(24), 13066; https://doi.org/10.3390/app132413066 - 07 Dec 2023
Viewed by 491
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
Appl. Sci. 2023, 13(20), 11434; https://doi.org/10.3390/app132011434 - 18 Oct 2023
Viewed by 566
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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