International Journal of Vehicle Performance (4 papers in press)

Regular Issues

• Research on emotion recognition of drivers peripheral physiological signals based on LSTM  
  by Yaqi Fan, Yutong Lin, Xiaolin Cao, Qin Liang, Jing Chen, Wenchao Xu 
  Abstract: The development of automotive industry and the increase in car ownership have led to increasingly severe issues regarding driving safety. Intervention to adjust the drivers emotions is important to improve driving safety. Effective emotion recognition is a significant prerequisite and foundation for this. Physiological signals, generated by the activity of the human autonomic nervous system, reflect a persons physical and mental state objectively, can be used as feature signals for emotion recognition. In this paper, three peripheral physiological signals [namely electrocardiograph (ECG), blood volume pulse (BVP) and respiration (RSP)] and subjective evaluation data on driver emotions collected from preliminary experiments were used to construct a driver emotion recognition model using long short-term memory (LSTM). The results indicate that compared to Support Vector Machines (SVM), the driver emotion recognition model with LSTM as the core algorithm performs better in terms of both recognition speed and accuracy.
  Keywords: emotion recognition; peripheral physiological signals; driver emotions; long short-term memory; LSTM.
  DOI: 10.1504/IJVP.2025.10072428
   
  
• Development of a KPI-focused hybrid model for the Cummins QSB6.7 engine used in load haul dump vehicles  
  by Bismarck Louw, P. Stephan Heyns, Stephan Schmidt 
  Abstract: Diesel engines are vital in mining operations, powering machinery in harsh environments where reliability is critical to avoid costly downtimes and economic losses. This study presents a hybrid model for the Cummins QSB6.7 turbocharged diesel engine to optimise predictive maintenance and operational efficiency in load-haul-dump vehicles. Controlled experiments generated key performance indicator (KPI) data under varied loads, capturing thermal, pressure, power, and efficiency metrics via QuantumX and CANedge2 systems. Physics-based models were calibrated using global and local optimisation, with complex metrics like turbocharger rotational speed as design variables. Neural networks mapped operational data, such as engine speed and load, to these variables, enabling accurate KPI predictions with minimal input. The model achieved MAPEs of 6.21% for thermal, 5.08% for pressure, and 4.12% for power, demonstrating strong predictive accuracy and practical applicability in mining contexts. These results underscore the models potential to significantly reduce unplanned downtimes and associated economic losses.
  Keywords: hybrid diesel engine models; key performance indicators; physics-based Cummins QSB; mining; vehicle performance.
  DOI: 10.1504/IJVP.2025.10072954
   
  
• Longitudinal control of an autonomous vehicle using fixed-time optimal fast terminal sliding mode on Arduino  
  by Najlae Jennan, El Mehdi Mellouli, Rachid Naoual 
  Abstract: This study proposes a fixed-time optimal fast terminal sliding mode control (OFTSMC) for precise and robust longitudinal control of an autonomous vehicle. The controller ensures fast convergence and improved performance, with its parameters optimised using particle swarm optimisation (PSO). The approach begins with modelling the vehicles longitudinal dynamics, followed by the design of the proposed controller. Stability is ensured through a Lyapunov based fixed-time convergence analysis. The proposed method is validated through MATLAB/Simulink simulations and real-world experiments on a four-wheel drive (4WD) vehicle using an Arduino Uno. Both results confirm the effectiveness of the proposed controller, demonstrating accurate tracking and strong robustness, with up to 76% reduction in error variance compared to traditional methods, for practical applications in vehicle dynamics control.
  Keywords: Arduino; longitudinal control; autonomous vehicle; fast terminal sliding mode controller; particle swarm optimisation; PSO; fixed-time convergence.
  DOI: 10.1504/IJVP.2025.10073384
   
  
• An improved fuzzy logic-based vehicle dynamics state estimation framework via coordination of extended Kalman filter and artificial neural networks  
  by Volkan Bekir Yangin, Yaprak Yalcin, Ozgen Akalin 
  Abstract: This paper proposes a combined estimation algorithm (CEA) designed to estimate the unknown yaw rate and side-slip angle values of a tactical vehicle while navigating at various speeds during a NATO double lane change manoeuvre, using known states. The CEA includes three independent modules: an extended Kalman filter (EKF), artificial neural networks (ANN), and fuzzy logic (FL). The EKF is based on a single-track nonlinear vehicle model, while the ANN using trained data. Both modules take the front axle steering angle as their single input and operate simultaneously in coordination with a novel fuzzy logic (FL) module, which integrates the outputs of both the ANN and EKF, utilising practical rules and membership functions derived from experience and experimental data to enhance prediction performance. Simulations demonstrated that the proposed CEA improves the estimation accuracy by between 8% and 59% for all error metrics, compared to the EKF and ANN alone.
  Keywords: extended Kalman filter; EKF; artificial neural networks; ANN; fuzzy logic; FL; state estimation; vehicle dynamics.
  DOI: 10.1504/IJVP.2025.10073435