Karel Kural

Vehicle dynamics can be deeply affected by various tyre operating conditions, including thermodynamic and wear effects. Indeed, tyre temperature plays a fundamental role in high performance applications due to the dependencies of the cornering stiffness and potential grip in such conditions. This work is focused on the evaluation of a potentially improved control strategy’s performance when the control model is fed by instantaneously varying tyre parameters, taking into account the continuously evolving external surface temperature and the vehicle boundary conditions. To this end, a simplified tyre thermal model has been integrated into a model predictive control strategy in order to exploit the thermal dynamics’ dependents within a proposed advanced ABS control system. We evaluate its performance in terms of the resulting braking distance. In particular, a non-linear model predictive control (NMPC) based ABS controller with tyre thermal knowledge has been integrated. The chosen topic can possibly lay a foundation for future research into autonomous control where the detailing of decision-making of the controllers will reach the level of multi-physical phenomena concerning the tyre–road interaction.

Professional truck drivers frequently face the challenging task of manually backwards manoeuvring articulated vehicles towards the loading bay. Logistics companies experience costs due to damage caused by vehicles performing this manoeuvre. However, driver assistance aimed to support drivers in this special scenario has not yet been clearly established. Additionally, to optimally improve the driving experience and the performance of the assisted drivers, the driver assistance must be able to continuously adapt to the needs and preferences of each driver. This paper presents the VISTA-Sim, a platform that uses a virtual reality (VR) simulator to develop and evaluate personalized driver assistance. This paper provides a comprehensive account of the VISTA-Sim, describing its development and main functionalities. The paper reports the usage of VISTA-Sim through the scenario of parking a semi-trailer truck in a loading bay, demonstrating how to learn from driver behaviours. Promising preliminary results indicate that this platform provides means to automatically learn from a driver’s performance. The evolution of this platform can offer ideal conditions for the development of ADAS systems that can automatically and continuously learn from and adapt to an individual driver. Therefore, future ADAS systems can be better accepted and trusted by drivers. Finally, this paper discusses the future directions concerning the improvement of the platform.

In this paper, a new control strategy for the active steering of a trailers of longer and heavier vehicle combinations is proposed to improve both low speed maneuverability and high speed stability. A novelty of the approach is in the use of a single controller structure for all velocities using a gain scheduling method for optimal performance at any velocity. To achieve such a control objective, the problem is initially formulated as a path following problem and subsequently transformed into a tracking problem using a reference model. To support controller design, a generic nonlinear model of a double articulated vehicle, based on a single track model, is employed. The proposed systematic design approach allows to easily adjust the controller for additional trailers or different dimensions, in which only some of the towed vehicles are allowed to steer. The performance of the controller is verified on a high-fidelity multi-body model for evidencing the practical applicability of the approach. Simulation results show substantial reduction of both, the swept path width and tail swing for low speed, and the rearward amplification for high speed.