Forthcoming and Online First Articles

International Journal of Automation and Logistics

International Journal of Automation and Logistics (IJAL)

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 Articles will not be published until the final proofs are validated by their authors.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 Automation and Logistics (2 papers in press)

Regular Issues

  • Feasibility of autonomous vehicles in Canada: a literature review   Order a copy of this article
    by Yue Cheng, Faith-Michael Uzoka 
    Abstract: This paper explores the feasibility of deploying fully autonomous vehicles in Canada in relation to legal liabilities. This is done by a systematic and iterative literature review of articles dating from 2010 to 2018. The factors of current self-guidance technology level are the legal framework in existence that deals with induced harm by autonomous vehicles, global viewpoints expressed regarding regulations of autonomous vehicles, and the status of Canadian legal aspects in recognising the difficulties of public autonomous vehicles usage. Discussion is presented with careful consideration, indicating the high possibility of restricted and limited public deployment in the foreseeable future. For long term deployment, there are too many factors to consider for a solution to be accurate.
    Keywords: autonomous vehicles; legal liabilities; Canada; collision avoidance.
    DOI: 10.1504/IJAL.2021.10039184
     
  • Fast adaptive algorithm for visual target tracking in complex environment   Order a copy of this article
    by Huakang Chen, Haiying Liu, Jason Gu, Lixia Deng, Juanting Zhou 
    Abstract: Aiming at the problem of poor performance of kernel correlation filtering tracking algorithm, a new multi-feature fusion tracking algorithm is proposed under the framework of kernel correlation filtering. Firstly, the improved algorithm integrates pyramid histogram of oriented gradients and colour names feature extraction to improve the appearance features of potential targets. Secondly, the adaptive update rate is introduced to guarantee the tracking accuracy when the appearance of the potential target changes. Finally, the improved algorithm is tested on the object tracking benchmark video sequence. The comparison results show that this method is superior to some classical methods in tracking accuracy and speed, especially in the natural complex environment.
    Keywords: visual tracking; feature representation; robot vision; complex environment.
    DOI: 10.1504/IJAL.2021.10041800
     

 

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