Supporting energy-efficient driving is essential for sustainable mobility in electric vehicles (EVs), where operational eco-driving (or ecodriving) can significantly reduce energy consumption. This study investigates the effects of two types of ecodriving displays—an Instantaneous Consumption Display (ICD) and an Optimal Speed Display (OSD)—on energy consumption and energy-related situation awareness (Energy Dynamics Awareness, EnDynA). In the EcoSimLab EV simulator, N = 94 participants were assigned to the ICD, OSD, or a control condition and completed multiple driving trials with varying situation complexity. Improvement, defined as the difference between the display and baseline conditions, in EnDynA and eco-driving performance, was greater in the display groups than in the control. The OSD, providing predictive speed recommendations by dynamic programming, was particularly effective in more complex driving scenarios. The ICD, offering real-time consumption feedback, facilitated learning in simpler scenarios. Findings underscore the importance of adaptive ecodriving support systems, balancing real-time feedback with predictive guidance.

Although interpretable controllers are widely used in vehicle systems, they have received limited attention as models of human driving behavior. This study explores whether two such control strategies, a preview-augmented Proportional-Integral-Derivative (PID) controller and a constraint-based Model Predictive Control (MPC) framework, can model human longitudinal driving behavior when adapted via Bayesian optimization. A structured dataset of human driving behavior, recorded with participants in a driving simulator, was used to train and evaluate both controllers across acceleration, deceleration, and cruising scenarios. MPC achieved lower overall deviation and more consistent performance across trials. These findings highlight the potential of combining interpretable control architectures with data-driven parameter adaptation to model human driving behavior effectively.

Driving simulators often lack fundamental components needed for accurate simulation of energy dynamics. We introduce EcoSimLab, a comprehensive electric vehicle driving simulation framework consisting of (1) a simulation of electric vehicle energy dynamics, (2) an optimization-based approach of structuring eco-driving behaviors, (3) a synthetic driver module as versatile benchmark model to analyze human behavior. Guided by fundamentals of energy modeling and considerations on human action regulation, we further present the development of the EcoDrivingTestPark, an exemplary set of energy-relevant scenarios to enable the analysis of individual differences in eco-driving and intervention effects (e.g., HMIs). To generate a first characterization of driving behavior, we conducted two empirical studies with human ( <?TeX $N_\text{S1}~=~31$?> Math 1 , <?TeX $N_\text{S2a}~=~41$?> Math 2 ) and synthetic drivers ( <?TeX $N_\text{S2b}~=~3$?> Math 3 ). Results indicate substantial variations in driver behavior and considerable challenges for human drivers to achieve synthetic driver performance. Implications for augmenting human action regulation in eco-driving are discussed.

On-demand ridepooling (ODR) can transform public transport by addressing urban challenges. However, to motivate usage, ODR should satisfy users’ psychological needs. The present study investigated to what extent needs predict ODR use, and to what extent ODR need satisfaction differs from key transportation modes. We conducted a longitudinal study spanning three months with weekly online surveys, focusing on nighttime ODR in an urban area. Longitudinal data were available from N = 29 participants. Results showed that need fulfillment significantly predicted ODR use, especially perceived competence. Discrete dimensions analyses showed that autonomy and competence were significantly predicting higher ODR usage. Comparing ODR and public bus, significant differences were found in all need dimensions; ODR performed consistently better. In comparing ODR and car, the only significant difference was monetary related; ODR was perceived as more cost-effective. In conclusion, psychological needs shape ODR usage and are crucial for designing such services.

Activity trackers are promising tools to increase the motivation to be physically active, thus strengthening users’ physical constitution and potentially preventing cardiovascular diseases. To establish behavioral changes with such beneficial consequences, prolonged continuous use of trackers seems to be necessary. However, the usage behavior of many tracker users is characterized by interruptions or complete discontinuation after only a few months. Which factors determine individual usage trajectories is still unclear. This research sheds light on user diversity to investigate how inter-individual differences are related to reasons for usage interruptions and permanence of abandonment. Results based on a survey of N = 159 former users revealed that usage motives regarding self-determination theory, domain-specific personality traits (affinity for technology interaction), and interaction variables (dependency effect, trust in activity tracker measurement) were related to specific reasons for usage interruptions. Moreover, highly autonomous usage motivation and high trust were linked to more fragile abandonment decisions.

When interacting with artificial intelligence (AI) in the medical domain, users frequently face automated information processing, which can remain opaque to them. For example, users with diabetes may interact daily with automated insulin delivery (AID). However, effective AID therapy requires traceability of automated decisions for diverse users. Grounded in research on human-automation interaction, we study Subjective Information Processing Awareness (SIPA) as a key construct to research users’ experience of explainable AI. The objective of the present research was to examine how users experience differing levels of traceability of an AI algorithm. We developed a basic AID simulation to create realistic scenarios for an experiment with N = 80, where we examined the effect of three levels of information disclosure on SIPA and performance. Attributes serving as the basis for insulin needs calculation were shown to users, who predicted the AID system’s calculation after over 60 observations. Results showed a difference in SIPA after repeated observations, associated with a general decline of SIPA ratings over time. Supporting scale validity, SIPA was strongly correlated with trust and satisfaction with explanations. The present research indicates that the effect of different levels of information disclosure may need several repetitions before it manifests. Additionally, high levels of information disclosure may lead to a miscalibration between SIPA and performance in predicting the system’s results. The results indicate that for a responsible design of XAI, system designers could utilize prediction tasks in order to calibrate experienced traceability.

The design of effective energy interfaces for electric vehicles needs an integrated perspective on the technical and psychological factors that together establish real-world vehicle energy efficiency. The objective of the present research was to provide a transdisciplinary synthesis of key factors for the design of energy interfaces for battery electric vehicles (BEVs) that effectively support drivers in their eco-driving efforts. While previous research tends to concentrate on the (visual) representation of common energy efficiency measures, we focus on the design of action-integrated metrics and indicators for vehicle energy efficiency that account for the perceptual capacities and bounded rationality of drivers. Based on this rationale, we propose energy interface examples for the most basic driving maneuvers (acceleration, constant driving, deceleration) and discuss challenges and opportunities of these design solutions.

Successful coping with technology is relevant for mastering daily life. Based on related conceptions, we propose affinity for technology interaction (ATI), defined as the tendency to actively engage in intensive technology interaction, as a key personal resource for coping with technology. We present the 9-item ATI scale, an economical unidimensional scale that assesses ATI as an interaction style rooted in the construct need for cognition (NFC). Results of multiple studies (n > 1500) showed that the scale achieves good to excellent reliability, exhibits expected moderate to high correlations with geekism, technology enthusiasm, NFC, self-reported success in technical problem-solving and technical system learning success, and also with usage of technical systems. Further, correlations of ATI with the Big Five personality dimensions were weak at most. Based on the results, the ATI scale appears to be a promising tool for research applications such as the characterization of user diversity in system usability tests and the construction of general models of user-technology interaction.

Advancing range-information user interfaces towards enhanced usability is a continuous task in electric vehicle development. The objective of the present research was (1) to examine experienced trustworthiness of a typical range-information user interface, (2) to test a newly constructed trustworthiness scale, (3) to examine the relationship of experienced trustworthiness to individual usable range (i.e., comfortable range), and (4) to identify possibilities for further improvement of range-information user interfaces. Data from N = 74 participants of a large-scale electric vehicle field trial were analyzed. Results show that experienced trustworthiness of the range estimation system is related to a higher comfortable range. Moreover, users developed several suggestions on how to further improve the user interface. The results imply that perceived trustworthiness should be considered as a benchmark for evaluating range-information user interfaces and that users should have flexible options to adjust the parameters of the range estimation system.