Matthias G. Arend

RWTH Aachen University · Chair and Institute of Industrial Engineering and Ergonomics

In search and rescue missions, teleoperated rovers equipped with sensor technology are deployed into harsh environments to search for targets. To support the search task, unimodal/multimodal cues can be presented via visual, acoustic and/or haptic channels. However, human operators often perform the search task in parallel with the driving task, wh...

Humans often interact with avatars in video gaming, workplace, or health applications, for instance. The present research studied object affordances from an avatar’s perspective. In two experiments, participants responded to objects with a left/right keypress, indicating whether the objects were upright or inverted. Task-irrelevant objects’ handles...

Although the implications of increasingly automated road transport for driver behavior are often studied from the perspective of safety and comfort, automation is also expected to increase energy efficiency and thus contribute to environmental sustainability. However, drivers’ interaction with automated systems that optimize the vehicle’s energy ef...

Cross-classified models accommodate data structures that have more than one cluster variable, which are not nested in each other but overlap. They simultaneously consider all clustering variables. This allows one to study effects on several levels at once. Cross-classified data structures are common in various field of applied research (e.g., resea...

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 ele...

Hybrid electric vehicles (HEVs) can contribute to sustainable transport. Yet, their real-world energy efficiency depends on HEV drivers' eco-driving behaviour. Eco-driving knowledge is key for successful eco-driving. The present research focused on the role of perceived strategy knowledge (know-how) versus technical system knowledge (know-why) in a...

Eco-driving can essentially be regarded as driver behavior targeted towards increased energy efficiency. As such, eco-drivers have an impact on fuel efficiency when selecting a vehicle (strategic eco-driving), selecting routes (tactical eco-driving) as well as selecting eco-driving strategies (operational eco-driving). On the operational level, a k...

More energy efficient and sustainable systems become increasingly widespread in automotive applications (e.g., hybrid electric vehicles; HEVs). Yet, their real-world energy efficiency strongly depends on driver behaviour and, often, showing optimal eco-driving behaviour is challenging especially if energy dynamics are not sufficiently represented i...

The growing demand for individualized consumer goods and the increase in production volume variations raise the level of complexity in assembly systems. As a result, people that work in such assembly systems have to cope with the flexibility requirements with respect to the assembly processes. The first step in complexity management is to measure t...

The demand for individualized products increases the complexity in assembly. This poster presents the development of a complexity measure for the work of assembly teams that work in One-Piece-Flow assembly systems. It is based on the Shannon Entropy complexity measurement approach. The measure is implemented into a 3D simulation model. Moreover, th...

The estimation of power in two-level models used to analyze data that are hierarchically structured is particularly complex because the outcome contains variance at two levels that is regressed on predictors at two levels. Methods for the estimation of power in two-level models have been based on formulas and Monte Carlo simulation. We provide a ha...

One potential contributor to mitigating the CO2 emissions caused by road transport is eco-driving. Ecodriving encompasses all driver behaviors performed to reduce the vehicle's energy consumption. Drivers' optimal on-road interaction with the kinetic energy resources is particularly relevant for eco-driving success. Hence, the question is what info...

Hybrid electric vehicles (HEVs) have the potential to accomplish high energy efficiency (i.e., low fuel consumption) given that drivers apply effective ecodriving control strategies (i.e., ecodriving behavior). However, HEVs have a relatively complex powertrain and therefore require a considerable knowledge acquisition process to enable optimal eco...

Hybrid electric vehicles (HEVs) can significantly contribute to sustainable road transport, yet driver behavior has a marked effect on actual energy efficiency (i.e., the ultimate sustainability effect). The objective of the present research was to examine ecodriving motivation of HEV drivers. To this end, we recruited 39 HEV drivers with above-ave...

Objective: The objective of the present research was to understand drivers’ interaction patterns with hybrid electric vehicles’ (HEV) eco-features (electric propulsion, regenerative braking, neutral mode) and their relationship to fuel efficiency and driver characteristics (technical system knowledge, ecodriving motivation). Background: Ecodriving...

Hybrid electric vehicles (HEVs) can help to reduce transport emissions; however, user behaviour has a significant effect on the energy savings actually achieved in everyday usage. The present research aimed to advance understanding of HEV drivers' ecodriving strategies, and the challenges for optimal user-energy interaction. We conducted interviews...