Multimodal in-vehicle infotainment systems offer drivers a range of non-driving-related functions but can increase visual-manual and cognitive task demand, compromising road safety. Therefore, it is important to estimate the secondary task demand of these systems early in the development process. To do so, the Box Task combined with a Detection Response Task (BT + DRT) was developed as a straightforward laboratory method. The BT is used to quantify the visual-manual task demand, while the DRT is capable of assessing cognitive demand. However, previous studies showed that difficult cognitive secondary task demand led to a similar decrease in performance in the BT to that found in the easy visual-manual demand. Therefore, this study aimed to enhance the BT’s sensitivity to visual-manual demand and discriminability from cognitive demand by increasing the difficulty of the BT. Additionally, the effects of increased BT difficulty on DRT metrics, self-assessed mental workload and secondary task performance were examined. In total, N = 39 participants performed the BT + DRT with varying BT difficulty levels (easy, moderate and difficult), secondary task types (visual-manual vs. cognitive) and secondary task difficulty levels (easy vs. difficult). The results indicated that lateral variability at moderate and difficult BT levels was the BT metric with the largest and most consistent effect sizes for assessing visual-manual secondary task demand and to discriminate from performance impairments resulting from cognitive task demand. At both BT levels, the DRT is also capable of effectively assessing cognitive demand, either through response time or the number of omissions. For self-assessed workload, only slight increases in ratings were observed for higher BT difficulty levels. There were only minor changes in secondary task performance, such as slightly slower responses, during more difficult BT levels. Consequently, a higher BT difficulty than previously used is recommended for the BT + DRT paradigm.

AbstractAutomated driving is continuously evolving and will be integrated more and more into urban traffic in the future. Since urban traffic is characterized by a high number of space-sharing conflicts, the issue of an appropriate interaction with other road users, especially with pedestrians and cyclists, becomes increasingly important. This chapter provides an overview of the research project “KIRa” (Cooperative Interaction with Cyclists in automated Driving), which investigated the interaction between automated vehicles and cyclists according to four project aims. First, the investigation of body posture as a predictor of the cyclists’ starting process. Second, the development of a VR cycling simulation and validation in terms of perceived criticality and experience of presence. Third, the experimental evaluation of a drift-diffusion model for vehicle deceleration detection. And fourth, the investigation of factors affecting cyclists’ gap acceptance. With these research aims, it was the project’s intention to contribute to a better understanding of the cyclists’ perception of communication signals and to improve the ability of automated vehicles to predict cyclists’ intentions. The results can provide an important contribution to the cooperative design of the interaction between automated vehicles and cyclists.

AbstractThis chapter describes central cooperative activities in the research priority program Cooperatively Interacting Vehicles (CoInCar). If the whole research program CoInCar can be seen as a wheel, which is turning research questions into answers, knowledge and hopefully progress for society, the individual research projects described in the other chapters could be seen as spokes of the wheel, and the aspects described in this chapter as an informal cooperative hub of the wheel. Starting with common essential definitions, a use case catalogue was derived and documented. Based on that, cooperation and interaction pattern were sketched and documented into a pattern database. While the details of the research hub described here are specific for this DFG priority program, the general principles of a research hub could be transferred to any other research and development activity.

The Box Task combined with a Detection Response Task (BT + DRT) is a relatively new and easy-to-use method to assess in-vehicle system demand, consisting of a visual-manual task (BT) and a cognitive task (tactile DRT). Currently, little is known regarding the sensitivity of the BT + DRT for different types and difficulty levels of secondary tasks. Therefore, the present study evaluated the BT + DRT's sensitivity compared to the Lane Change Test (LCT), which is an ISO-standardized test method. Fifty-two participants engaged in a visual-manual (Surrogate Reference Task) and a cognitive secondary task (counting task) across two levels of difficulty while performing a PC version of the BT + DRT and LCT. The BT parameters, especially the standard deviation of box position and size, were sensitive to visual-manual secondary task demand. Moreover, the results showed that the DRT is a sensitive method to assess cognitive demand, supporting previous findings. In contrast, the mean deviation of the adaptive reference lane used as the standard LCT parameter was not as effective in discriminating between visual-manual and cognitive tasks. Hence, to distinguish between different types and levels of secondary task demand, the BT + DRT is more accurate than the standard LCT version. Future studies should investigate how an increased BT difficulty can further improve the sensitivity to visual-manual and cognitive secondary task demand.

The Box Task combined with a Detection Response Task (BT + DRT) is a relatively less investigated but promising method for evaluating visual-manual and cognitive task demand due to the interaction with in-vehicle information systems while driving. The BT includes the tracking of a dynamic box whose size and position follow a sinusoidal pattern with uniform amplitudes and frequencies. However, it is unclear whether participants are able to predict and adapt to these uniform dynamics, which might lead to a reduced sensitivity of the BT + DRT. Within the present study, it was aimed to examine differences in BT + DRT performance depending on uniform and non-uniform BT dynamics. A laboratory study was conducted with N = 41 participants. The experimental conditions differed in the type and difficulty level of the secondary tasks as well as in the BT dynamics (uniform, varying amplitude, varying frequency). While the uniform BT dynamics could be more predictable, the non-uniform BT dynamics were designed slightly easier in their difficulty using a lower frequency or amplitude. The results revealed no performance benefits when performing uniform BT dynamics compared to non-uniform BT dynamics. The frequency BT condition was related to a significantly lower variability of box position and higher gaze duration on the secondary task compared to the uniform BT dynamics. These findings suggest that participants are not or only negligible able to adapt to the uniform BT dynamics. Therefore, it is recommended to use the uniform BT dynamics as suggested and implemented in previous studies.

The assessment of task demand caused by in-vehicle systems is crucial to avoid distraction while driving. The Box Task (BT) in combination with a tactile Detection Response Task (DRT) provides a method for measuring both visual-manual and cognitive secondary task demand. In the present study, the impact of cognitive, auditory-verbal tasks on the BT + DRT performance was investigated. Thirty-two participants had to perform an easy as well as a difficult version of an n-back task and a memory scanning task while simultaneously performing the BT + DRT. There was only a slight effect of cognitive task demand on the BT performance parameters, while the DRT proved to be highly sensitive to cognitive task demand. Therefore, it is assumed that the method is suitable for a differentiated measurement of task demand dimensions.

Automated driving is an ever-fast developing technology which is going to change the traffic system fundamentally. This development raises the question about how to interact in urban, less-regulated situations involving automated vehicles (AV) and vulnerable road users (VRU) like cyclists and/or pedestrians. Thus, it is essential to gain an understanding of interactions between VRU and current, mainly non-automated vehicles. This study focusses on cyclist-vehicle interaction and raises the questions whether there are typical interaction parameters. To address these issues, data of a previous Naturalistic Cycling Study (NCS), was re-analyzed. The selected sample consisted of 11 cyclists (ranging from 24 to 48 years, 8 males, 3 females). The subjects’ bicycles were instrumented with two cameras (forward view and face of the rider) that recorded a four-week lasting cycling period. In total, 69 interactions between cyclists and vehicles in less-regulated traffic context were analyzed. As a result of this descriptive approach, we identified common cyclist maneuvers (e.g., avoiding) and behavioral parameters (e.g., keeping constant speed) in the light of different infrastructural context. The discussion addresses the functionality of different behavioral patterns and the arising challenges for AV technology.

The evaluation of the distraction potential of secondary task activities while driving has traditionally been focused on visual-manual tasks. In previous years, different test protocols have been developed and standardized to evaluate the distraction effects of in-vehicle information systems while driving. However, the assessment of cognitive distraction has not received much attention in this context. In the present paper, a new method, that combines a two-dimensional tracking task (the so called ‘Box Task’) with the Detection Response Task, is proposed. Thus, visual-manual as well as cognitive distraction effects can be assessed. Two evaluation studies are summarized that confirm the ability of this new evaluation method to distinguish between different types and levels of distraction.

The use of advanced in-vehicle information systems (IVIS) and other complex devices such as smartphones while driving can lead to driver distraction, which, in turn, increases safety-critical event risk. Therefore, using methods for measuring driver distraction caused by IVIS is crucial when developing new in-vehicle systems. In this paper, we present the setup and implementation of the Box Task combined with a Detection Response Task (BT+DRT) as a tool to assess visual-manual and cognitive distraction effects. The BT+DRT represents a low-cost and easy-to-use method which can be easily implemented by researchers in laboratory settings and which was validated in previous research. Moreover, at the end of this paper we describe the experimental procedure, the data analysis and discuss potential modifications of the method.•The setup and implementation of the Box Task combined with a Detection Response Task (BT+DRT) is described.•The method allows for measuring visual-manual and cognitive distraction of drivers.•The BT+DRT is a cost-effective and easy-to-use method that can be implemented in laboratory settings or driving simulators.

Students' evaluations of teaching quality in higher education are difficult to validate. The criteria of validity (e.g., instructors' judgments) frequently show a low reliability or validity. In order to examine multidimensional student evaluations, student data can be compared with assessments given by instructors and third persons. The research project "Consultation of university instructors for improving teaching quality focusing social-cognitive conflicts" ("SoKonBe") aims at improving teaching quality of university instructors. In the project, the instructors themselves, their students as well as external instructors evaluate lectures-the external instructors by means of video recorded lecture sequences. Teaching quality was assessed by means of the Heidelberg Inventory for Evaluation of Teaching (HILVE II).In traditional approaches, it was necessary to ask for experts (colleagues, instructors, researchers and counselors) to attend the lectures to gain ratings from them. This was costly; additionally, the teaching process could be changed by the presence of experts. If the experts not attended the lectures, the assessment was based on indirect evidence, as on teaching material, reports of students, rumors or on observable achievement in research and administration. As a new approach, we used recorded lecture sequences implemented and administrated by an evaluation web portal for a better, easier, faster, more cost-effective and more valid external evaluation of teaching quality. External instructors (here used as experts) could evaluate lectures based on selected sequences.In order to use the web portal, the external instructors registered themselves from their computer via a socio-demographic questionnaire. After that, they found the assigned password-protected video sequences (15 minutes of the whole lecture) in their user account. Sequences could be repeated for more detailed observation. At the end, the external instructors were guided to the online HILVE II questionnaire to evaluate the teaching.In addition to this front-end user area, there was a back-end administrator area for academic staff. Here, videos were assigned to the external instructors and data were retrieved. The program almost completely manages the email communication. The external instructors receive after the registration automated notifications, final assessment as well as a reminder in the case of delay. The portal offers the instructors the opportunity to receive a feedback to compare their self-perception with the perception of others.Up to now, the data from 44 instructors evaluated by 1,714 students have been stored. With the help of the web portal for these 44 instructors and their teaching, 326 evaluations from 86 external instructors (professors from 32 German universities) were collected. On average, the student assessments correlated slightly more highly with those of external instructors (r = .30) than student assessments with those of instructors (r =.26). Between the assessments of external instructors and instructors there was nearly no correlation (r = .08). This pattern supports former findings on student-instructor-expert correlations ([1]). The use of the web portal has led to a cost-effective and easy to administrate external measurement of data, in comparison with the traditional approaches, which are more costly and difficult to organize.