International Journal of Vehicle Autonomous Systems (5 papers in press)

Regular Issues

• A new authentication model for vehicular ad hoc networks with optimisation strategy  
  by Sanjay S. Deshmukh 
  Abstract: The VANET enhancing road safety by distributing messages amongst vehicles. The effective method of serving vehicles devoid of choosing a path between destination and source is periodic broadcasting. However, network performance suffers issues with hidden nodes and broadcasting storms. The new authentication model for vehicular Ad Hoc networks involves System Initialization, RSU registration phase, Vehicle registration phase, Authentication phase, Vehicle joining and leaving phase. Initially, employing a cryptographic hash function in the suggested approach. Specifically, proposed authentication message verification stage is conducted by signcryption with ECC based model for encryption, which has lower computational overhead and less bandwidth consumption. During encryption, generated key is optimally tuned via HBE-SSA (Hybrid Bald Eagle Shark Smell Algorithm), which make the network can operate for more extended period. The overall result demonstrates that the HBE-SSA achieved better KPA attack value about 0.11322 compared to existing algorithms BES, SSO, DOA and HBA respectively.
  Keywords: VANET; signcryption; key generation; elliptic curve; HBE-SSA algorithm.
  DOI: 10.1504/IJVAS.2025.10070687
   
  
• A review on learning-based and model predictive control approaches for cooperative adaptive cruise control system  
  by Iman Tahbazzadeh Moghaddam, Moosa Ayati, Amir Taghavipour 
  Abstract: The need for governments to provide fast and safe transportation is increasing day by day. However, this rising demand for transportation has resulted in higher traffic intensity, longer travel times, increased road accidents, and greater consumption of fossil fuels. Advanced driver assistance systems (ADAS) can help mitigate these issues by enhancing driver-vehicle interaction with the environment. Furthermore, the development of vehicle communication networks has led to the emergence of a new generation of ADAS called cooperative adaptive cruise control (CACC) systems, which hold great promise for automating navigation. Despite their potential, the performance of CACC systems designed so far faces significant challenges that severely impact their effectiveness. This paper first describes the architecture and control concepts of CACC systems. It then reviews various learning-based and model predictive control approaches employed in recent years to design CACC systems, aiming to enhance transportation safety, fuel efficiency, and passenger comfort.
  Keywords: cooperative adaptive cruise control system; connected vehicles; model predictive control; learning-based controller; reinforcement learning; deep neural networks.
  DOI: 10.1504/IJVAS.2025.10071584
   
  
• Unmanned soil sampling vehicle path planning based on improved genetic algorithm with dynamic A* algorithm  
  by Bin Huang, Jianmin Wu, Qiyue Tang, Nuorong Yang 
  Abstract: Unmanned sampling vehicles encounter path planning challenges due to complex terrain and vehicle kinematics constraints; hence, this paper proposes an Improved Genetic Algorithm (IGA) path planning method. The study frames the soil sampling path optimization problem as a single depot multi-traveler problem with capacity constraints (SDMTSP) and develops a three-chain encoding. Strategies such as a curvature penalty mechanism, adaptive cross-variance strategy, and dynamic population management enhance global search capability. A* algorithm employs an improved heuristic function and eight-neighbourhood search for local optimization, alongside segmented spline interpolation for path smoothing. Experiments demonstrate that the paths formulated by the improved genetic algorithm are shortened by 19.20%, 20.12%, and 3.59% compared to the particle swarm algorithm (PSO), traditional genetic algorithm (GA), and swarm ant algorithm (ACO), respectively, while also exhibiting strong robustness. This method can offer an efficient and safe path planning scheme for the automated operation of unmanned soil sampling vehicles.
  Keywords: unmanned soil sampling vehicle; multi-travelling dealer problem; path planning; improved genetic algorithms; improved A* algorithm.
  DOI: 10.1504/IJVAS.2025.10071860
   
  
• A biometric-based intelligent monitoring system for early detection and prevention of driver fatigue and distraction  
  by Mohammed Ben Tarief, Sakher Alaqbawe, Suleiman Abu-Ein, Hisham Almujafet 
  Abstract: Computer information technology is increasingly used in safety applications. Driver health and conduct are crucial as they significantly impact road safety. Drivers, especially those with chronic conditions, face heightened risks. A Driver Monitoring System (DMS) utilizes sensors and algorithms to monitor behaviour and physiology in real-time to detect fatigue, distraction, or impairment. The proposed system tracks blood pressure, oxygen levels, and heart rate, alerting the driver or intervening when necessary. The aim of this paper is to suggest ways to consider certain factors that are crucial to ensure the safety of drivers. By implementing a system that keeps track of the driver's condition, a significant number of accidents can be avoided. There are many software packages available in today's industry that offer different driver monitoring devices.
  Keywords: biometric identification; smartwatch; distracted driving detection; safe mode control module; heart rate; blood pressure; oxygen content; real-time alerting; driver safety systems; intelligent transportation systems.
  DOI: 10.1504/IJVAS.2025.10071861
   
  
• Continuous-time optimal control for trajectory planning under uncertainty  
  by Ange Valli, Shangyuan Zhang, Abdel Lisser 
  Abstract: This paper presents a continuous-time optimal control framework for generating reference trajectories for autonomous vehicles. The method developed involves chance constraints for modelling uncertainty. A previous work (Zhang et al., 2022a) presented such a model in discrete time and designed for urban driving scenarios only.We obtain better results in continuous time: it models urban driving scenarios with faster computation and better capacity to capture uncertainty as it is less likely to violate the problem’s constraints in risky scenarios. It is also robust for optimal control of highway scenarios.
  Keywords: vehicle autonomous systems; trajectory planning; urban driving scenarios; chance-constraint optimisation; continuous-time optimal control; stochastic modelling; autonomous vehicles safety.
  DOI: 10.1504/IJVAS.2025.10072190