Modeling for Intelligent Vehicles

Special Issue Editors

School of Rail Transportation, Soochow University, Suzhou 215006, China
Interests: connected automated vehicles; digital twins; trajectory planning; end-to-end system

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Guest Editor
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China
Interests: traffic signal control; connected automated vehicles; traffic flow theory; traffic simulation; machine learning; trajectory predictions
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Special Issue Information

Dear Colleagues,

Intelligent vehicles incorporating advanced V2X technologies, leveraging sophisticated data analytics and algorithms, have significant potential to mitigate traffic accidents, enhance societal well-being, and optimize the operational efficiency of transportation networks. Future directions of intelligent vehicles may encompass advancements in autonomous driving, enhanced connectivity/interaction, and integration of AI to enhance various aspects of mobility.

With increasing automation and connectivity, intelligent vehicles require more advanced decision-making and motion planning schemes, environment perceptions, and cooperative control methods to operate in challenging traffic scenarios. Especially in dense road traffic, signalized intersections, highway ramps, tunnels, parking, etc., modeling for intelligent vehicles is crucial to maintain a certain level of their performance while guaranteeing safety at the same time.

Moreover, intelligent vehicle fields gradually adopt end-to-end algorithm frameworks that leverage raw sensor data to formulate vehicle motion strategies. These move beyond traditional focuses on isolated tasks like detection and motion prediction. End-to-end systems, as opposed to modular pipelines, offer advantages through integrated feature optimization across perception and planning domains and also bring opportunities and challenges in modeling for intelligent vehicles.

The main aim of this Special Issue is to seek high-quality submissions highlighting recent breakthroughs in intelligent vehicle technologies and applications, including autonomous vehicle behaviors, perceptions, motion plannings, cooperative controls, connected technologies, end-to-end systems, etc.

Potential topics include but are not limited to the following categories:

  • Perception models integrating sensor data;
  • Prediction- and optimization-based planning and control;
  • Control Strategies for hybrid-driving traffic flow;
  • Simulation and validation frameworks for intelligent vehicle systems;
  • Machine learning and AI applications in vehicle modeling;
  • Safety, reliability, and robustness of modeling approaches;
  • Case studies and real-world applications of intelligent vehicle modeling;
  • Traffic management for intelligent vehicles;
  • End-to-end autonomous driving.

Dr. Weike Lu
Dr. Zhihong Yao
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. World Electric Vehicle Journal is an international peer-reviewed open access monthly 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 1400 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

  • autonomous vehicle behaviors
  • perceptions
  • trajectory prediction and planning
  • cooperative con-trols
  • connected technologies
  • end-to-end systems

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Published Papers (3 papers)

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Research

15 pages, 6323 KiB  
Article
Modeling and Validation of Acoustic Comfort for Electric Vehicle Using Hybrid Approach Based on Soundscape and Psychoacoustic Methods
by Keysha Wellviestu Zakri, Raden Sugeng Joko Sarwono, Sigit Puji Santosa and F. X. Nugroho Soelami
World Electr. Veh. J. 2025, 16(2), 64; https://doi.org/10.3390/wevj16020064 - 22 Jan 2025
Viewed by 1409
Abstract
This paper evaluated the acoustic characteristics of electric vehicles (EVs) using both psychoacoustic and soundscape methodologies by analyzing three key psychoacoustic parameters: loudness, roughness, and sharpness. Through correlation analysis between perceived values and objective parameters, we identified specific sound sources requiring improvement, including [...] Read more.
This paper evaluated the acoustic characteristics of electric vehicles (EVs) using both psychoacoustic and soundscape methodologies by analyzing three key psychoacoustic parameters: loudness, roughness, and sharpness. Through correlation analysis between perceived values and objective parameters, we identified specific sound sources requiring improvement, including vehicle body acoustics, wheel noise, and acceleration-related sounds. The relationship between comfort perception and acoustic parameters showed varying correlations: loudness (0.0411), roughness (2.3452), and sharpness (0.9821). Notably, the overall correlation coefficient of 0.5 suggests that psychoacoustic parameters alone cannot fully explain human comfort perception in EVs. The analysis of sound propagation revealed elevated vibration levels specifically in the driver’s seat area compared to other vehicle regions, identifying key targets for improvement. The research identified significant acoustic events at three key frequencies (50 Hz, 250 Hz, and 450 Hz), requiring in-depth analysis to determine their sources and understand their effects on the vehicle’s NVH characteristics. The study successfully validated its results by demonstrating that a combined approach using both psychoacoustic and soundscape parameters provides a more comprehensive understanding of passenger acoustic perception. This integrated methodology effectively identified specific areas needing acoustic refinement, including: frame vibration noise during rough road operation; tire-generated noise; and acceleration-related sound emissions. Full article
(This article belongs to the Special Issue Modeling for Intelligent Vehicles)
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14 pages, 1431 KiB  
Article
Optimizing Energy Supply for Full Electric Vehicles in Smart Cities: A Comprehensive Mobility Network Model
by Victor Fernandez, Virgilio Pérez and Rosa Roig
World Electr. Veh. J. 2025, 16(1), 5; https://doi.org/10.3390/wevj16010005 - 27 Dec 2024
Cited by 1 | Viewed by 1219
Abstract
The integration of Full Electric Vehicles (FEVs) into the smart city ecosystem is an essential step towards achieving sustainable urban mobility. This study presents a comprehensive mobility network model designed to predict and optimize the energy supply for FEVs within smart cities. The [...] Read more.
The integration of Full Electric Vehicles (FEVs) into the smart city ecosystem is an essential step towards achieving sustainable urban mobility. This study presents a comprehensive mobility network model designed to predict and optimize the energy supply for FEVs within smart cities. The model integrates advanced components such as a Charge Station Control Center (CSCC), smart charging infrastructure, and a dynamic user interface. Important aspects include analyzing power consumption, forecasting urban energy demand, and monitoring the State of Charge (SoC) of FEV batteries using innovative algorithms validated through real-world applications in Valencia (Spain) and Ljubljana (Slovenia). Results indicate high accuracies in SoC tracking (error < 0.05%) and energy demand forecasting (MSE ~6 × 10−4), demonstrating the model’s reliability and adaptability across diverse urban environments. This research contributes to the development of resilient, efficient, and sustainable smart city frameworks, emphasizing real-time data-driven decision-making in energy and mobility management. Full article
(This article belongs to the Special Issue Modeling for Intelligent Vehicles)
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12 pages, 1949 KiB  
Article
Analysis of Public Acceptance and Influencing Factors of Cooperative Vehicle Infrastructure Technology
by Wei Bai, Yuan Yuan and Linheng Li
World Electr. Veh. J. 2024, 15(11), 500; https://doi.org/10.3390/wevj15110500 - 31 Oct 2024
Viewed by 807
Abstract
Cooperative vehicle infrastructure technology has emerged as a cutting-edge and indispensable trend within the transportation sector. While addressing the supply-side requisites of the technology, it is equally important to investigate its demand-side response. To investigate the public acceptance of cooperative vehicle infrastructure technology [...] Read more.
Cooperative vehicle infrastructure technology has emerged as a cutting-edge and indispensable trend within the transportation sector. While addressing the supply-side requisites of the technology, it is equally important to investigate its demand-side response. To investigate the public acceptance of cooperative vehicle infrastructure technology and its influencing factors, this paper constructs an extended Technology Acceptance Model (TAM). Then, the paper employs the structural equation model (SEM) to validate the path hypotheses of the model, and pinpoints the variables that significantly influence the intention to use the technology. Moreover, the Bayesian network (BN) model is utilized to assess the magnitude of the effects of diverse influencing factors on the acceptance of the technology. The research findings can provide recommendations for the government to expedite the promotion and implementation of cooperative vehicle infrastructure technology. Full article
(This article belongs to the Special Issue Modeling for Intelligent Vehicles)
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