As digital transformation continues, everyday technologies will fundamentally change: they will become proactive, autonomous and more and more opaque for humans.
This research project examines how cooperation between humans and algorithmic agents can and ought to be designed, with regard to three potentially competing objectives: performance, satisfaction and accountability. Different types of human-algorithm-cooperation examples were created followed by exploring their impact on the efficiency and effectiveness of the result, the work satisfaction and wellbeing of the humans involved and societal and regulatory implications.
The project furthermore addresses broader issues of how to design human-algorithm-cooperation between the priorities of industry, workers, and society at large.
Road traffic is a social environment where drivers’ actions affect not only themselves but also other road users, including pedestrians and cyclists. With the increasing automation of vehicles, interactions in traffic will transform, relying on cooperation between vehicles, environments, and users through advanced technologies.
This project investigates how automation is changing social interaction in traffic and how sensory technologies can shape these interactions. Using multimodal sensors, including in-vehicle sensors and wearables, the study will explore how traffic situations are perceived by drivers and other road users, aiming to identify behaviors that are considered prosocial and contribute to a harmonious and safe traffic environment. The research will also develop new communication methods and interaction concepts for cooperation between humans and automated vehicles, addressing issues such as reliability, norm compliance, and decision-making. Ultimately, the project will provide a test environment and guidelines for designing interactive technologies that encourage prosocial behavior in traffic, using both qualitative and quantitative methods to assess social interactions in various traffic scenarios.
THESIS PROJECTS
Master Thesis
Driving automation aims to enhance comfort, safety, and traffic flow by removing the human driver from the control loop. However, the human experience of commuting involves more than just reaching a destination or assuming the role of a driver. Factors like route selection, driving style, and courtesy towards fellow road users are integral to the driving experience but often overlooked in automated vehicle design.
This thesis explores the desires of passengers in highly automated cars to participate in vehicle control. A video vignette study (N=16) was initiated to understand users’ needs for cooperation. Consequently, a Human-Machine Interface for cooperative control was developed and evaluated in a VR simulation study (N=15). Results indicate that most participants expressed a desire for cooperative driving, albeit varying with the driving situation. Moreover, allowing cooperation improved passengers’ overall experience by satisfying psychological needs for autonomy, security, competence, and relatedness.