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Applied Sciences
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Autonomous Vehicles and Robotics—2nd Edition
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Autonomous Vehicles and Robotics—2nd Edition

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

Deadline for manuscript submissions: 20 February 2026 | Viewed by 2409

Special Issue Editors

Prof. Dr. Nan Ma

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Guest Editor
Faculty of Information and Technology, Beijing University of Technology, Beijing 100124, China
Interests: interactive cognition; machine vision; intelligent driving; knowledge discovery and intelligent system
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Taohong Zhang

E-Mail Website
Guest Editor
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: computer modeling and simulation calculation; artificial intelligence; knowledge engineering; image processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yang Yang

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: computer vision; medical image analysis; action recognition; intelligent robot collaboration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The automobile industry is undergoing a profound transformation driven by three major disruptions: the transition from internal combustion engines to electric engines; the shift from human drivers to autonomous driving; and the evolution from traditional ownership business models to mobility-as-a-service platforms. These transformations will not only reshape the automobile industry in the coming decade, with trillion-dollar new market opportunities, but also offer humanity a better future by accelerating the adoption of clean energy, enabling cheaper and safer transportation services, and enhancing the use of urban infrastructures.

However, due to the application of immature and unreliable software, fatal accidents have weakened trust in these systems. In this regard, we must improve their safety, security, and reliability. The IEEE International Symposium on Autonomous Vehicle Software (AVS) creates a unique venue for researchers, engineers, industry players, and policy makers to present the latest advancements and innovations in theoretical work, open source projects, software applications, and regulations in autonomous vehicle software.

The IEEE International Symposium on Autonomous Vehicle Software (AVS2025) aims to unite researchers and scientists from artificial intelligence, robotics, and various application areas to discuss problems and solutions in this field, identify new issues, and shape future directions for research.

This Special Issue will introduce research work on autonomous vehicle software and robot perception and interaction, covering areas such as vulnerability assessment, risk analysis, attack and threat models, visual understanding, machine vision, intelligent interaction, autonomous-vehicle-embodied intelligence, and LLM applications, among others.

We warmly invite submissions in the form of original scientific research articles for this Special Issue. We also extend our appreciation to all presenters and speakers in advance for their participation at this conference, and look forward to a stimulating exchange of ideas.

The AVS2025 conference will be held at the American University of Sharjah, which is about 20 minutes from Dubai, the United Arab Emirates, from November 24 to 26, 2025.

Prof. Dr. Nan Ma
Prof. Dr. Taohong Zhang
Prof. Dr. Yang Yang
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 250 words) can be sent to the Editorial Office for assessment.

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

  • autonomous vehicle
  • intelligent interaction
  • self-driving environment perception
  • machine vision
  • visual understanding

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Related Special Issue

Published Papers (3 papers)

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Research

21 pages, 2733 KB  
Article
Construction of an Intelligent Risk Identification System for Highway Flood Damage Based on Multimodal Large Models
by Jinzi Zheng, Zhiyang Liu, Chenguang Li, Hanchu Zhou, Erlong Lou, Yaqi Li and Bingou Xu
Appl. Sci. 2025, 15(23), 12782; https://doi.org/10.3390/app152312782 - 3 Dec 2025
Cited by 1 | Viewed by 427
Abstract
Under the increasing threat of extreme weather events, road infrastructure faces significant risks of flood-induced damage. Traditional manual inspection methods are insufficient for modern highway emergency response, which requires higher efficiency and accuracy. To enhance the precision and accuracy of flood damage identification, [...] Read more.
Under the increasing threat of extreme weather events, road infrastructure faces significant risks of flood-induced damage. Traditional manual inspection methods are insufficient for modern highway emergency response, which requires higher efficiency and accuracy. To enhance the precision and accuracy of flood damage identification, this study proposes an intelligent recognition system that integrates a multimodal large language model with a structured knowledge base. The system constructs a professional repository covering eight typical categories of flood damage, including roadbed, pavement, and bridge components, with associated attributes, visual features, and mitigation strategies. A vectorized indexing mechanism enables fine-grained semantic retrieval, while task-specific templates and prompt engineering guide the multimodal model, such as Qwen-VL-Max, which extracts risk elements from image–text inputs and generating structured identification results with expert recommendations. The system is evaluated on a real-world highway flood damage dataset. The results show that the knowledge-enhanced model performs better than the baseline and prompt-optimized models. It reaches 91.5% average accuracy, a semantic relevance score of 4.58 out of 5, and 85% robustness under difficult conditions. These results highlight the strong domain adaptability and practical value for real-time flood damage assessment and emergency response. Full article
(This article belongs to the Special Issue Autonomous Vehicles and Robotics—2nd Edition)
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29 pages, 2910 KB  
Article
A Vehicular Traffic Condition-Based Routing Lifetime Control Scheme for Improving the Packet Delivery Ratio in Realistic VANETs
by Jonghyeon Choe, Youngboo Kim and Seungmin Oh
Appl. Sci. 2025, 15(22), 12017; https://doi.org/10.3390/app152212017 - 12 Nov 2025
Viewed by 574
Abstract
Packet delivery in vehicular ad hoc networks degrades under realistic road dynamics, where mobility and local density vary over time and across road layouts. This study revisits route lifetime control in AODV and introduces Vehicular Traffic Condition-Based AODV, which adjusts the Active Route [...] Read more.
Packet delivery in vehicular ad hoc networks degrades under realistic road dynamics, where mobility and local density vary over time and across road layouts. This study revisits route lifetime control in AODV and introduces Vehicular Traffic Condition-Based AODV, which adjusts the Active Route Timeout and the Delete Period Constant online at each HELLO reception using locally observable cues on neighbor density and short-term speed variation. The design is empirically informed by OpenStreetMap and SUMO mobility with OMNeT++/Veins/INET co-simulation. The analysis highlights two recurrent patterns that guide the method: (i) an intermediate neighbor-density range—roughly from the mid-teens to about twenty neighbors—where average speed tends to vary more rapidly; and (ii) a distribution of short-term speed-change magnitudes, sampled at the instants of HELLO reception, that is concentrated within a narrow interval with a light upper tail. Accordingly, the proposed method increases or decreases route-entry lifetimes with heightened responsiveness inside this density range, while applying conservative updates elsewhere to mitigate oscillations. Evaluation across multiple vehicular-traffic conditions spanning three fleet sizes (200, 300, 400 vehicles) and three speed-limit settings (10, 20, 30 km/h) demonstrates consistent packet delivery ratio gains over conventional AODV and close tracking of the best static lifetime configurations identified per condition. The gains are attributable to timely pruning of unstable paths and sustained retention of stable paths, which increases valid forwarding opportunities without centralized coordination. Full article
(This article belongs to the Special Issue Autonomous Vehicles and Robotics—2nd Edition)
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29 pages, 3996 KB  
Article
Demand Assessment and Integration Feasibility Analysis for Advanced and Urban Air Mobility in Illinois
by Vasileios Volakakis, Christopher Cummings, Laurence Audenaerd, William M. Viste and Hani S. Mahmassani
Appl. Sci. 2025, 15(22), 11901; https://doi.org/10.3390/app152211901 - 8 Nov 2025
Viewed by 973
Abstract
Advanced and Urban Air Mobility (AAM and UAM) represent emerging transportation concepts that involve the use of novel aircraft technologies to transport passengers and cargo within urban, regional, and intra-regional environments. These systems may include Electric Vertical Take-off and Landing (eVTOL) aircraft, Short [...] Read more.
Advanced and Urban Air Mobility (AAM and UAM) represent emerging transportation concepts that involve the use of novel aircraft technologies to transport passengers and cargo within urban, regional, and intra-regional environments. These systems may include Electric Vertical Take-off and Landing (eVTOL) aircraft, Short Take-off and Landing (STOL) aircraft, and unmanned aerial vehicles (UAVs), which are being considered for a range of applications including passenger transport, cargo delivery, and other specialized operations. This study introduced a state-specific analytical framework that integrates different methodologies and data to enable a more precise evaluation of AAM viability in the State of Illinois, compared to generic national or global assessments, capturing the state’s unique mobility patterns, infrastructure constraints, and demographic distributions. One of the main goals is to provide a comprehensive evaluation of the potential implications—both challenges and opportunities—associated with AAM and UAM operations. The analysis examines potential impacts on mobility, infrastructure, economic development, and public services, with particular emphasis on identifying key considerations for policy development. The research framework categorizes use cases into two broad types: AAM for the transportation of people and cargo, and AAM for functional applications such as emergency response, agriculture, and infrastructure monitoring. The study provides a detailed quantitative assessment of passenger air taxi services, including demand estimation, business model feasibility analysis, integration effects on existing transportation systems, and infrastructure requirements. For other AAM applications, the analysis identifies operational considerations, regulatory implications, and potential barriers to implementation, establishing a foundation for future detailed evaluation. Full article
(This article belongs to the Special Issue Autonomous Vehicles and Robotics—2nd Edition)
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