Forthcoming Articles

International Journal of Vehicle Performance

International Journal of Vehicle Performance (IJVP)

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 Articles will not be published until the final proofs are validated by their authors.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 Performance (3 papers in press)

Regular Issues

  • An intelligent road perception system for advanced driver assistance: combining object detection and distance estimation   Order a copy of this article
    by Ouafae Abarkan, Walid Jebrane, Nabil El Akchioui 
    Abstract: Artificial intelligence (AI) techniques, such as deep learning and computer vision, have seen increasing adoption in the automotive industry, driving a rapid transformation of the sector in recent years. Intelligent, autonomous, and efficient decision-making in vehicles has become feasible thanks to these technologies. In this paper, we present an advanced application for the detection of road traffic-related objects, developed using the single shot multibox detector SSD300 algorithm. In addition to object recognition, our system integrates a distance estimation module that calculates the proximity between the vehicle and the identified objects. This dual functionality enhances road safety by supporting proactive collision avoidance and maintaining safe following distances in dynamic driving environments.
    Keywords: artificial intelligence; road objects; SSD300; distance estimation.
    DOI: 10.1504/IJVP.2026.10076499
     
  • Machine learning-based multi-objective optimisation of automotive aerodynamic add-ons   Order a copy of this article
    by Lingxi Deng, Xueliang Pang, Yanming Du, Xingren Zheng 
    Abstract: This study aims to optimise a vehicle aerodynamic add-on using machine learning and computational fluid dynamics (CFD) to reduce drag (Cd) and lift coefficients (Cl), thereby improving fuel efficiency and stability. Based on the Ahmed model, a duckwing-like add-on was designed with five key geometric dimensions as variables. Among the radial basis, extreme learning machine, support vector machine, and BP neural network models, the back propagation (BP) neural network was found to have the best prediction accuracy. It was then combined with the non-dominated sorting genetic algorithm (NSGA-II) for multi-objective optimisation to generate the Pareto frontier. The results demonstrated significant improvements in aerodynamic performance, achieving up to 24.83% reduction in Cd and 15.5% reduction in Cl. Analysis using extreme randomised tree (ET) and random forest (RF) algorithms indicates that the add-on angle is a critical factor influencing drag coefficient (Cd), while cloud visualisation analysis of the Ahmed model further validated its effectiveness in controlling wake flow. This approach offers a novel method for optimising vehicle aerodynamics through multi-objective optimisation.
    Keywords: multi-objective optimisation; machine learning; BP neural networks; genetic algorithms; computational fluid dynamics; CFD; Ahmed vehicle models; aerodynamic add-ons.
    DOI: 10.1504/IJVP.2026.10076782
     
  • Optimisation of pulse and glide technique to enhance the energy saving control methodology in low power utility vehicle   Order a copy of this article
    by Venkatesh Natarajan, Arockia Julias Arulraj, Jeyakumar Ponniah Daniel, Prabu Krishnasamy 
    Abstract: Autonomous connected vehicles execute complex algorithms with or without driver interventions to implement fuel-saving methods of driving. One such energy-saving drive mode, pulse and glide (PnG) is redefined and a suitable methodology is proposed for evaluating its effect during real-time driving conditions. Various parameters like acceleration, velocity changes, city driving, highway driving, load and other dynamic factors are experimentally studied at different operating conditions. Key parameters influencing the energy-saving method are identified and the relation between them is analysed by data-driven approach. Optimisation of the energy saving parameters is executed, framed as an algorithm and evaluated on Indian road conditions. Significant improvement in fuel saving by up to 16% is achieved for four-wheeler passenger car in highway and almost negligible saving in city. Energy assessment driving cycle for automation test is arrived for vehicles powered by electrical energy and internal combustion engine.
    Keywords: fuel saving; eco-driving; pulse and glide; PnG; energy saving methodology; Indian road conditions; vehicle test cycle.
    DOI: 10.1504/IJVP.2026.10077854
     

 

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