Forthcoming and Online First 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 Articles will not be published until the final proofs are validated by their authors.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

  • Automatic driving systems considering trajectory tracking and lateral stability control of distributed electric drive semi-trailer truck   Order a copy of this article
    by Zhaowen Deng, Si Deng 
    Abstract: Aiming at the self-driving semi-trailer automobile truck under extreme working conditions, the trajectory tracking accuracy is poor, easy to occur lateral instability and other dangerous problems. In this paper, a hierarchical control system for trajectory tracking and lateral stability is proposed by combining distributed electric drive technology. Firstly, a linear three-degree-of-freedom dynamics reference model is established for obtaining the ideal values of vehicle state quantities; secondly, the upper trajectory tracking controller is designed based on model predictive control (MPC) and PID control theory for accurately tracking the reference trajectory. The lower layer designs the direct yaw moment controller (DYC) based on the sliding mode control theory, decides the additional yaw moment required by the tractor, and allocates the additional yaw moment by using the quadratic programming (QP) method; finally, the co-simulation platform of TruckSim and MATLAB/Simulink is built to verify the effectiveness of the controller.
    Keywords: distributed electric drive; semi-trailer truck; model predictive control; trajectory tracking; lateral stability.

  • Insights of computer vision-based techniques: perspective transformation and sliding window approach for lane line detection in autonomous vehicles   Order a copy of this article
    by Madhuri Pagale, Sunanda Mulik, Richa Purohit, Anuradha Thakare 
    Abstract: The technique of sliding windows and perspective modification are employed in this work for lane identification. Then, binary thresholding is applied. We utilise state-of-the-art methods to obtain directional gradients and gradient magnitudes, enhancing the data necessary for lane identification. An essential first step is to change your frame of view so that you may examine things from a different angle. To effectively follow previously recognised lane pixels, we apply the sliding window method to lane recognition. Our method guarantees a smooth incorporation of lane information by superimposing the detected lane lines onto the initial image using a mask. We present a new metric for lane quality assessment that provides a quantitative measure of detection accuracy and is based on the mean and variance. Autonomous vehicles and driver-assistance systems stand to benefit greatly from this comprehensive strategy, which aims to elevate lane identification to a new level of excellence.
    Keywords: autonomous vehicle; road lane lines; lane detection; lanes; road; safety; DAS; deep learning; perspective transformation; sliding window technique; algorithm; etc.

  • Soft actor-critic with automatically adjusted entropy for autonomous exploring in unknown environments   Order a copy of this article
    by Walid Jebrane, Nabil El Akchioui 
    Abstract: This paper presents an in-depth analysis of the soft actor-critic (SAC) algorithm, specifically focusing on its automatically adjusted temperature (alpha) variant, within the context of autonomous navigation in unexplored environments. We developed an exploration system that integrates the SAC-alpha algorithm to autonomously navigate and map unfamiliar terrains, employing a strategic waypoint selection mechanism to optimise movement toward target objectives. The SAC-alpha framework was customised and enhanced to address the challenges posed by the lack of pre-existing environmental maps and the presence of complex obstacles. The efficacy of SAC-alpha was rigorously evaluated in simulated environments, utilising metrics such as success rate, navigation efficiency, and obstacle avoidance performance. Results from these evaluations demonstrate that SAC-alpha outperforms the twin delayed deep deterministic policy gradient (TD3) algorithm, exhibiting superior capability in overcoming local optima, efficiently navigating complex environments, and ensuring safety throughout the navigation process.
    Keywords: autonomous robot navigation; deep reinforcement learning; DRL; soft actor-critic; SAC; automatically adjusted entropy; exploring unknown environments.

 

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