Forthcoming and Online First Articles

International Journal of Vehicle Autonomous Systems

International Journal of Vehicle Autonomous Systems (IJVAS)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Vehicle Autonomous Systems (2 papers in press)

Regular Issues

  • Analysis of recent trends and developments in assisted and automated driving systems: a systematic review   Order a copy of this article
    by Shivaji Thorat, Ramesh Pawase 
    Abstract: Surface road transportation is an essential part of our daily life, enabling us to commute to work, school, hospital, and many places ubiquitously. This leads to an increased need of private transportation. Also, road safety is potentially impacted by driving under the influence and increased usage of mobiles during driving Distracted driving is becoming a major reason for increased road accidents today. Thus, there seems a need of advanced driver assistance systems (ADAS) and automated driving (AD) in vehicles to ensure human safety in risky road environments. It has several benefits such as increased road safety, traffic jam reduction, low fuel consumption, and minimal human intervention in safety-critical driving conditions. This review paper delivers an overview of recent technological trends and advancements that happened in ADAS/AD systems with its limitations (or challenges) and opportunities ahead.
    Keywords: advanced driver assistance systems; automated driving; autonomous vehicles; electrical and electronics architectures; road safety; sensor fusion and radars.
    DOI: 10.1504/IJVAS.2024.10063840
     
  • A decentralised asynchronous federated learning framework for autonomous driving   Order a copy of this article
    by Xiaoli Li, Ting Cai, Wei Xiong, Degang Xu 
    Abstract: Obtaining more information from other vehicles to accurately identify the road environment is an urgent issue for autonomous driving. However, collecting road environmental information directly from other vehicles may violate personal privacy. Federated learning can achieve multi-vehicle collaborative sensing of the road environment while protecting data privacy. We propose a decentralized asynchronous federated learning framework based on blockchain. Firstly, using blockchain to replace the central server of traditional federated learning architecture avoids the untrustworthy issues caused by the central architecture. Secondly, the blockchain module includes scoring contract units and incentive contract units to prevent malicious vehicle attacks, and designs fair incentive mechanisms to ensure the ecological health and sustainable development of federated learning. Thirdly, using the asynchronous federated learning algorithm, blockchain can immediately aggregate model updates from vehicles, greatly improving the overall training flexibility and real-time performance. Experimental results demonstrate the effectiveness of the proposed framework.
    Keywords: autonomous driving; blockchain; federated learning; asynchronous; decentralised.
    DOI: 10.1504/IJVAS.2024.10064091
     

 

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