International Journal of Vehicle Autonomous Systems (4 papers in press)

Regular Issues

• An ATMEGA 168-based semi-autonomous mobile forklift system with six degrees of freedom  
  by Philip Adewuyi, Opeyemi Owodolu 
  Abstract: A mobile forklift used in industries and factories contributes to an increase in productivity. However, most mobile forklifts have limited freedom of movement, especially, in tight or narrow work areas as well as difficulties in avoiding obstacles due to limited operational flexibility and maneuverability. This work focuses on the design and construction of a model of a semi-autonomous mobile forklift having six degrees of freedom using a four omni mecanum wheeled platform. Test results obtained show that vibration of internal mechanism, response time of components, and the nature of work terrain or surface all determine the performance responses of the loaded and unloaded forklift system during test. The longitudinal speed, transversal speed, and angular speed responses of the forklift are proportional to the input pulse signals supplied for the control purpose as determined by the microcontroller used as the central processing unit of the developed system. A prototype of this mobile forklift with four omni mecanum wheel is developed and presented in this work.
  Keywords: forklift; six degrees of freedom; mecanum; wheel; mechanical motion; vibration; internal mechanism; spin wheel; autonomous vehicle; PWM; pulse width modulation; angle of rotation.
  DOI: 10.1504/IJVAS.2025.10075518
   
  
• An innovative trajectory tracking and obstacle avoidance framework for autonomous underwater vehicles using adaptive and sparse attention-based DRL  
  by K. Aravind Kumar, Bharani Chandra Kumar Pakki 
  Abstract: Autonomous Underwater Vehicle (AUV) is highly utilised for civil and military, especially applied in complex underwater tasks. Several approaches have been proposed for accurately determining the complex underwater environment in AUV. The existing models are not efficiently utilised, which impact the simulation performance. It does not efficiently predict complex external disturbances to minimise the model accuracy. The work implements an Adaptive and Sparse Attention-based Deep Reinforcement Learning (ASA-DRL) approach to resolve the 3D path tracking and the obstacle avoidance problems. By employing the safety factors-aided reward functions, unwanted developments are eliminated. In addition, the ASA-DRL approachs hyperparameters are optimised by Randomly Revised Quokka Swarm Optimisation (RRQSO). The step number and number of episodes parameters are quickly tuned in the optimisation phase. The proposed approach attained an optimal energy consumption rate of 156%, 196% and 116% in terms of 32, 64 and 128 state sizes.
  Keywords: autonomous underwater vehicles; three-dimensional trajectory tracking and obstacle avoidance; adaptive and sparse attention-based deep reinforcement learning; randomly revised quokka swarm optimisation.
  DOI: 10.1504/IJVAS.2025.10075613
   
  
• Performance of controlled pneumatic suspension in ameliorating the quality of heavy trucks  
  by Fengxiang Song, Vanliem Nguyen, Li Zhang 
  Abstract: With the heavy trucks using the Passive Pneumatic Suspension Systems (PPSS), when the vehicle moves at high velocity on poor road surfaces, the drivers comfort is very poor while the vehicles ability to destroy the road surface is very large. To improve the driver comfort and reduce road damage, a dynamic model of the vehicle is established, then, PID and Fuzzy controls are studied to control the active damping values in the PPSS. The investigation shows that seat displacement, seat acceleration and road damage index of the vehicle with Fuzzy control are strongly decreased compared to PID control and PPSS under all vehicle operation conditions. Especially, these values decrease sharply when the vehicle moves at high velocities above 20 m/s on very poor road surfaces. Thus, Fuzzy control should be applied in the heavy trucks PPSS to further improve the operating efficiency of heavy trucks.
  Keywords: heavy truck's dynamic model; control pneumatic suspension; PID control; fuzzy control; vehicle quality.
  DOI: 10.1504/IJVAS.2026.10075614
   
  
• Multi-objective optimisation for charging infrastructure deployment of electric autonomous vehicles in intelligent manufacturing systems  
  by Lin Cheng, Jiantong Song, Guna Wang 
  Abstract: In this research, we present an all-encompassing optimisation approach for IMS that takes into account ecological, financial, and operational concerns while planning the charging infrastructure for EAVs. The Red Deer Algorithm (RDA) optimises by locating the optimal balance between exploration and exploitation, drawing inspiration from the mating behaviours of red deer in Scotland. The methodology incorporates detailed system architecture design, dynamic electricity pricing strategies, and realistic power flow modelling for various electric vehicle fast-charging station (EVFS) configurations. When comparing the RDA to PSO, the results show that the RDA achieves faster convergence and better solutions. Extensive case studies, such as the West Delta network in Egypt and the highway charging infrastructure on Hainan Island, prove that the model effectively ensures the right size and location of charging stations while also increasing voltage stability, decreasing power losses, and improving system efficiency.
  Keywords: EAVs; electric autonomous vehicles; charging infrastructure; multi-objective optimisation; power flow modelling; CO₂ emissions reduction; renewable energy integration; smart mobility; EVFS; PSO; particle swarm optimisation.
  DOI: 10.1504/IJVAS.2025.10075712