Implementing human factors within the design process of advanced driver assistance systems (ADAS)
This research project aims at developing a design approach for ADAS applications in which human factors (including stakeholder feedback and objective performance measures) are accounted for. Because the driving task is part of a complex (traffic) system, with a large number of interacting components, ADAS design is confronted with choices for which the influence on the system and the driving performance in particular, cannot be predicted. Therefore, providing relevant feedback in the early stages of the design process about the consequences of specific choices would increase the efficiency and safety of driver assistance systems.
Background and problem definition
The emerging trend in driver support systems is aimed at reducing requirements placed on the driver. By equipping vehicles with sensors, navigation and motion planning, the driving task is shared between human actors and the supporting assistance systems. Ultimately, by adding and improving cognition and control techniques, this could lead to autonomous vehicles in which the driving task is controlled by the vehicle and the responsibility is shifted towards the vehicle and its manufacturer.
Although legal issues and high infrastructural demands will prevent the introduction of such autonomous vehicles in the near future, research has already provided (semi-) automated concept cars in which no (or minimal) intervention of human actors is required.
Meanwhile, different assistance systems are already supporting the driver by means of sensory information (e.g. visibility aids or lane departure warnings), correction (e.g. anti-lock braking system or traction control) or even control (e.g. automatic parking).
A serious implication of the growing amount of these assistance systems in modern day cars is the unknown effect different types and quantities of information can have on the driving performance. Different studies have provided evidence of reduced users’ workload while supported by assistance systems (e.g. Stanton and Young, 2005). However, these studies did not take into account what effect a combination of support systems (and hence, with different configurations and amounts of information) can have on the drivers’ performance. Information which is of prior importance in order to produce safe and efficient cars in the future.
The present research is aimed at providing ADAS designers with insight in what influence different types and configurations of information, provided by the assistance systems, can have on the driving performance. Ultimately, our goal is to improve the efficiency and safety of driver assistance systems. This research aims to develop a design approach that allows designers to obtain relevant feedback during the design process, regarding the consequences of their choices. For this, we intend to set up an adaptive design environment in which both stakeholders’ preferences and driving performance can be measured, in order to bring about designs in which human factors are well accounted for.
Stanton, N.A., Young, M.S. (2005). Driver behaviour with adaptive cruise control. Ergonomics, 48(10), 1294-1313.
This research is part of the knowledge centre AIDA, a collaboration between TNO and the University of Twente.
Boris van Waterschoot, MSc / Phone +31 (0) 53 489 2266 / firstname.lastname@example.org
Boris M. van Waterschoot (1973) obtained his Master’s degree in Cognitive Psychology at the Nijmegen Institute for Cognition and Information (Radboud University, Nijmegen). His graduation research involved the influence of context on the Perception - Action cycle. It was found that an (automatic) tendency to imitate (which assumes a rigid cortical mapping) can be overcome by contextual rules. In his aim to bridge the gap between fundamental- and applied research, he has recently started his PhD project at the Center for Applications of Integrated Driver Assistance (AIDA, University of Twente), which involves the implementation of human factors in the design process of advanced driver assistance systems. This project aims to provide a common ground for both human factors and design communities in their objective to evaluate and design driver support systems.