Advancements in Autonomous Vehicles: Security, Optimization and Future Challenges

Special Issue Editor


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Guest Editor
School of Technology, Moulay Ismail University of Meknes, Meknes 50050, Morocco
Interests: IoT; cloud/fog computing; python; network programmability; operating systems; computer networks and network security
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Special Issue Information

Dear colleagues,

The integration of advanced technologies into autonomous vehicles (AVs) is revolutionizing transportation by enhancing security, optimizing performance, and tackling future challenges. This Special Issue presents cutting-edge research and experimental results, ranging from robust AI algorithms that bolster security to optimization techniques that improve route planning and energy efficiency.

Key areas of interest include innovations in cybersecurity, crucial for protecting AVs from potential vulnerabilities, and advanced sensor fusion methods that enhance perception and decision-making capabilities. Intelligent control systems are also highlighted, especially those designed for safe navigation in complex environments.

This Special Issue aims to delve into future challenges such as regulatory and ethical considerations, public acceptance, and the impact on urban planning. Case studies on practical applications of AVs in urban mobility, logistics, and public transportation provide valuable insights into the current and future potential of these technologies.

The development of simulation environments and standardization efforts is critical for fostering innovation and ensuring the reliability and safety of AV systems. Furthermore, research on predictive maintenance and fault detection emphasizes the importance of advanced data analytics in enhancing the performance and longevity of AVs. This Special Issue aims to pave the way for future advancements to be made in autonomous vehicle technology.

Dr. Nabil Benamar
Guest Editor

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Keywords

  • autonomous vehicles
  • AI algorithms
  • cybersecurity
  • navigation
  • regulatory and ethical considerations
  • public acceptance
  • urban planning
  • autonomous vehicle technology

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Published Papers (1 paper)

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Research

14 pages, 2331 KiB  
Article
Enhancing Weather Scene Identification Using Vision Transformer
by Christine Dewi, Muhammad Asad Arshed, Henoch Juli Christanto, Hafiz Abdul Rehman, Amgad Muneer and Shahzad Mumtaz
World Electr. Veh. J. 2024, 15(8), 373; https://doi.org/10.3390/wevj15080373 - 16 Aug 2024
Viewed by 992
Abstract
The accuracy of weather scene recognition is critical in a world where weather affects every aspect of our everyday lives, particularly in areas like intelligent transportation networks, autonomous vehicles, and outdoor vision systems. The importance of weather in many aspects of our life [...] Read more.
The accuracy of weather scene recognition is critical in a world where weather affects every aspect of our everyday lives, particularly in areas like intelligent transportation networks, autonomous vehicles, and outdoor vision systems. The importance of weather in many aspects of our life highlights the vital necessity for accurate information. Precise weather detection is especially crucial for industries like intelligent transportation, outside vision systems, and driverless cars. The outdated, unreliable, and time-consuming manual identification techniques are no longer adequate. Unmatched accuracy is required for local weather scene forecasting in real time. This work utilizes the capabilities of computer vision to address these important issues. Specifically, we employ the advanced Vision Transformer model to distinguish between 11 different weather scenarios. The development of this model results in a remarkable performance, achieving an accuracy rate of 93.54%, surpassing industry standards such as MobileNetV2 and VGG19. These findings advance computer vision techniques into new domains and pave the way for reliable weather scene recognition systems, promising extensive real-world applications across various industries. Full article
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