Triangulated investigation of trust in automated driving: Challenges and solution approaches for data integration

Characteristics of Ethical Leadership: Themes Identification Through Convergent Parallel Mixed Method Design From the Pakistan Context

“…In this study, we are using a "Separate approach" of relational dimension as we are independently analyzing the two strands of data (i.e., qualitative and quantitative) independent of each other and finally merged them together for the conclusion. Second, in the methodological dimension, this study uses the "Equal status approach, " where the strategy of this research was to equivalently understand both (qualitative and quantitative) epistemologies and extract deeper insights from them (Kalayci et al, 2021). Finally, this study uses the "simultaneous bidirectional dimension" approach, where we simultaneously and independently collected qualitative (literature review)-based data as well as the quantitative (openended questionnaire driven) data responses and separately analyzed them and then merged them together for concluding convergence and divergent interpretations and extractions from data.…”

Governing AI in Electricity Systems: Reflections on the EU Artificial Intelligence Bill

“…This can result in SOs making decisions (regarding balancing or investments) based on models that they do not understand or control, leading to questions regarding accountability for public spending, high electricity prices or network downtime (MIT, 2016;Doran et al, 2017). For accountability purposes and to prevent automation bias or "overtrusting" the program (Kalayci et al, 2021), it should be clear on what basis data and data-analyses decisions are made.…”

Driver Distraction Detection Methods: A Literature Review and Framework

“…For this reason, vehicle manufacturers, suppliers, start-ups, and researchers are devoting more and more resources to better understand and measure the causes of driver distraction and inattention. Thereby, they develop warning and prevention mechanisms for drivers [4] or increase the automation level of the vehicle to avoid dealing with driver distraction in the first place, e.g., with automated driving functionalities [5], [6]. Some modern vehicles above a certain vehicle class and equipment level may already have simple systems that can detect certain types of driver inattention, such as driver fatigue [7], and warn the driver accordingly.…”

“…Such systems are usually subsumed under the term driver assistance systems, which also includes vehicle automation systems such as adaptive distance keeping or lane keeping [8]. With the increasing automation of the driving function, more and more powerful systems will find their way into vehicles, with the aim of fully automated or autonomous driving [6]. Consequently, there is also increasing research and publication on driver distraction detection and related topics.…”