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Applied Sciences
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Intelligent Autonomous Vehicles: Development and Challenges
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Intelligent Autonomous Vehicles: Development and Challenges

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 3805

Special Issue Editors

Dr. Ronik Ketankumar Patel

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
Interests: shared autonomous vehicles; mobility on demand; travel behavior; disaster preparedness
Special Issues, Collections and Topics in MDPI journals
Dr. Apurva Pamidimukkala

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
Interests: construction safety; resilient infrastructure; smart and sustainable mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue on Intelligent Autonomous Vehicles: Development and Challenges in Applied Sciences. Intelligent Autonomous Vehicle development is fueled by continuous advancements in artificial intelligence, robotics, and sensor technologies, which promise to revolutionize transportation modalities in urban and rural settings.

This Special Issue aims to gather high-quality research addressing critical issues such as safety, reliability, regulatory frameworks, and ethical considerations in deploying autonomous vehicles. Contributions are expected to explore the societal impacts, infrastructure requirements, and economic implications associated with the widespread adoption of autonomous vehicles.

The topics of particular interest include, but are not limited to:

  • Autonomous vehicle technology advancements.
  • Public perception and acceptance of autonomous vehicles.
  • Regulatory frameworks and ethical considerations.
  • Human–machine interaction with autonomous vehicles.
  • Infrastructure requirements for autonomous vehicles.
  • Economic and societal impacts of autonomous vehicles.
  • Case studies on autonomous vehicle deployment.

We invite submissions encompassing rigorous theoretical and experimental studies, comprehensive reviews, and survey papers.

Dr. Ronik Ketankumar Patel
Dr. Apurva Pamidimukkala
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

  • autonomous vehicles
  • artificial intelligence
  • safety and reliability
  • technology advancements
  • regulatory frameworks
  • infrastructure requirements
  • economic and societal impacts

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Published Papers (3 papers)

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Research

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19 pages, 1714 KiB  
Article
Model Predictive Control-Based Energy-Lifetime Co-Optimization Strategy for Commercial Hybrid Electric Vehicles
by Yingbo Wang, Shunshun Qin, Wen Sun, Shuzhan Bai and Ke Sun
Appl. Sci. 2025, 15(16), 9027; https://doi.org/10.3390/app15169027 - 15 Aug 2025
Viewed by 235
Abstract
To address the issue of key component degradation in hybrid electric commercial vehicles under complex driving cycles negatively impacting system economy and durability, this paper proposes a model predictive control (MPC)-based energy management co-optimization strategy. Firstly, dynamic degradation models for the key components [...] Read more.
To address the issue of key component degradation in hybrid electric commercial vehicles under complex driving cycles negatively impacting system economy and durability, this paper proposes a model predictive control (MPC)-based energy management co-optimization strategy. Firstly, dynamic degradation models for the key components are established, enabling high-fidelity characterization of component health status. Secondly, a system-level model incorporating vehicle dynamics, power battery, and electric drive motor is developed, with the degradation feedback mechanism deeply integrated. Building on this foundation, an MPC-based energy management strategy for multi-objective optimization is designed. Its core functionality lies in the cooperative allocation of power sources within a rolling horizon framework: by integrating component degradation status as critical feedback into the control loop, the strategy proactively coordinates the optimization objectives between operational economy (minimization of equivalent energy consumption) and key component durability (degradation mitigation). Simulation results demonstrate that, compared to traditional energy management strategies, the proposed strategy significantly enhances system performance while ensuring vehicle drivability: equivalent energy efficiency improves by approximately 3.5%, component degradation is reduced by up to 87%, and superior state of charge (SOC) regulation capability for the battery is achieved. This strategy provides an effective control method for achieving intelligent, long-life, and high-efficiency operation of hybrid electric commercial vehicles. Full article
(This article belongs to the Special Issue Intelligent Autonomous Vehicles: Development and Challenges)
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31 pages, 28041 KiB  
Article
Cyberattack Resilience of Autonomous Vehicle Sensor Systems: Evaluating RGB vs. Dynamic Vision Sensors in CARLA
by Mustafa Sakhai, Kaung Sithu, Min Khant Soe Oke and Maciej Wielgosz
Appl. Sci. 2025, 15(13), 7493; https://doi.org/10.3390/app15137493 - 3 Jul 2025
Viewed by 715
Abstract
Autonomous vehicles (AVs) rely on a heterogeneous sensor suite of RGB cameras, LiDAR, GPS/IMU, and emerging event-based dynamic vision sensors (DVS) to perceive and navigate complex environments. However, these sensors can be deceived by realistic cyberattacks, undermining safety. In this work, we systematically [...] Read more.
Autonomous vehicles (AVs) rely on a heterogeneous sensor suite of RGB cameras, LiDAR, GPS/IMU, and emerging event-based dynamic vision sensors (DVS) to perceive and navigate complex environments. However, these sensors can be deceived by realistic cyberattacks, undermining safety. In this work, we systematically implement seven attack vectors in the CARLA simulator—salt and pepper noise, event flooding, depth map tampering, LiDAR phantom injection, GPS spoofing, denial of service, and steering bias control—and measure their impact on a state-of-the-art end-to-end driving agent. We then equip each sensor with tailored defenses (e.g., adaptive median filtering for RGB and spatial clustering for DVS) and integrate a unsupervised anomaly detector (EfficientAD from anomalib) trained exclusively on benign data. Our detector achieves clear separation between normal and attacked conditions (mean RGB anomaly scores of 0.00 vs. 0.38; DVS: 0.61 vs. 0.76), yielding over 95% detection accuracy with fewer than 5% false positives. Defense evaluations reveal that GPS spoofing is fully mitigated, whereas RGB- and depth-based attacks still induce 30–45% trajectory drift despite filtering. Notably, our research-focused evaluation of DVS sensors suggests potential intrinsic resilience advantages in high-dynamic-range scenarios, though their asynchronous output necessitates carefully tuned thresholds. These findings underscore the critical role of multi-modal anomaly detection and demonstrate that DVS sensors exhibit greater intrinsic resilience in high-dynamic-range scenarios, suggesting their potential to enhance AV cybersecurity when integrated with conventional sensors. Full article
(This article belongs to the Special Issue Intelligent Autonomous Vehicles: Development and Challenges)
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Review

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23 pages, 1517 KiB  
Review
Autonomous Vehicles in Rural Areas: A Review of Challenges, Opportunities, and Solutions
by Melika Ansarinejad, Kian Ansarinejad, Pan Lu, Ying Huang and Denver Tolliver
Appl. Sci. 2025, 15(8), 4195; https://doi.org/10.3390/app15084195 - 10 Apr 2025
Cited by 5 | Viewed by 2363
Abstract
The growing demand for equitable and efficient transportation solutions has positioned autonomous vehicles (AVs) as a transformative technology with significant potential for rural areas. This literature review examines the challenges and opportunities associated with AV deployment in rural environments, characterized by sparse infrastructure, [...] Read more.
The growing demand for equitable and efficient transportation solutions has positioned autonomous vehicles (AVs) as a transformative technology with significant potential for rural areas. This literature review examines the challenges and opportunities associated with AV deployment in rural environments, characterized by sparse infrastructure, diverse road conditions, and aging populations. Using a systematic analysis of field tests, simulation-based studies, and survey research, key obstacles are identified, including limited lane markings, unpaved roads, digital connectivity gaps, and user acceptance issues. The results highlight the critical role of advancements in sensor technology, localization methods, and edge computing in addressing these barriers. Additionally, strategic infrastructure modifications, such as enhanced road signage and reliable communication systems, are essential for AV integration. This paper emphasizes the need for tailored AV solutions to meet the specific requirements of rural settings, including adaptability to adverse weather conditions and mixed traffic environments. Insights into public perception reveal the importance of trust-building initiatives and community engagement to foster widespread acceptance. The findings provide actionable recommendations for policymakers, industry leaders, and infrastructure operators, focusing on scalable deployment strategies, policy adaptations, and sustainable solutions. By addressing these challenges, AVs enhance mobility, safety, and accessibility, transforming rural transportation networks into more equitable and efficient systems. This review serves as a foundational reference for future research, charting pathways for the integration of AVs in rural contexts. Full article
(This article belongs to the Special Issue Intelligent Autonomous Vehicles: Development and Challenges)
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