Abstract

Driverless cars are a hot topic in today’s industry where several vehicle manufacturers try to create a reliable system for automated driving. The advantages of highly automated vehicles are many, safer roads and a lower environmental impact are some of the arguments for this technology. However, the notion of highly automated cars give rise to a large number of human factor issues regarding the safety and reliability of the automated system as well as concern about the driver’s role in the system."br/""br/"The purpose of this study was to explore the effects of systematic variations in traffic complexity and external time pressure on decision-making time in a simulated situation using a web-based testing platform. A secondary focus was to examine whether measures of cognitive performance and driver attitudes have an effect on decision-making time."br/""br/" The results show that systematic variations in both time pressure and traffic complexity have an effect on decision-making time. This indicates that drivers are able to adapt their decision-making to facilitate the requirements of a certain situation. The results also indicate that intelligence; speed of processing and driver attitudes has an effect on decision-making time.

Abstract

Appropriate task routing and assignment is an important, but often overlooked, element in crowdsourcing research and practice. In this paper, we explore and evaluate a mechanism that can enable matching crowdsourcing tasks to suitable crowd-workers based on their cognitive abilities. We measure participants’ visual and fluency cognitive abilities with the well-established Kit of Factor-Referenced Cognitive Test, and measure crowdsourcing performance with our own set of developed tasks. Our results indicate that participants’ cognitive abilities correlate well with their crowdsourcing performance. We also built two predictive models (beta and linear regression) for crowdsourcing task performance based on the performance on cognitive tests as explanatory variables. The model results suggest that it is feasible to predict crowdsourcing performance based on cognitive abilities. Finally, we discuss the benefits and challenges of leveraging workers’ cognitive abilities to improve task routing and assignment in crowdsourcing environments. Document type: Conference object