Supporting personalisation of ADAS through driver characteristics - a design science study

Abstract

The identification of driver characteristics is an important step to allow the adaptation of Advanced Driver Assistance Systems (ADAS) to individual driver needs, and thus increase system performance and trust in the system function. However, most ADAS are designed for the average driver and we investigated which driving characteristics are relevant for personalised features, and how to derive them using Requirement elicitation. We conducted a design study in two iterative cycles and investigated the objectives by performing semi-structured interviews and literature review. From the interviews and literature review a a list of characteristics was created. The list was later used to propose a way to derive the characteristics from a driver using a Human-Machine Interaction interface.