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.

Contemporary assistance systems support a broad variety of tasks. When they provide information or instruction, the way they do it has an implicit and often not directly graspable impact on the user. System design often forces static roles onto the user, which can have negative side effects when system errors occur or unique and previously unknown situations need to be tackled. We propose an adjustable augmented reality-based assistance infrastructure that adapts to the user’s individual cognitive task proficiency and dynamically reduces its active intervention in a subtle, not consciously noticeable way over time to spare attentional resources and facilitate independent task execution. We also introduce multi-modal mechanisms to provide context-sensitive assistance and argue why system architectures that provide explainability of concealed automated processes can improve user trust and acceptance.