In highly automated driving (HAD), it is still an open question how machines can safely hand over control to humans, and if an advance notice with additional explanations can be beneficial in critical situations. Conceptually, use of formal methods from AI – description logic (DL) and automated planning – in order to more reliably predict when a handover is necessary, and to increase the advance notice for handovers by planning ahead at runtime, can provide a technological support for explanations using natural language generation. However, in this work we address only the user’s perspective with two contributions: First, we evaluate our concept in a driving simulator study (N=23) and find that an advance notice and spoken explanations were preferred over classical handover methods. Second, we propose a framework and an example test scenario specific to handovers that is based on the results of our study.

One challenge in highly automated driving is the safe transfer of control (ToC). A safe ToC requires estimating the take-over time depending on the driver’s state in different environmental conditions, to adapt the timing and design of the ToC request. We introduce environmental complexity as one factor that affects the ToC time. In a driving simulator experiment (N=12), the participants drove in five scenes having different environmental complexities (i.e. density and height of the background objects) with and without a secondary task. The results revealed that the ToC time is proportional to the environmental complexity. Thus, in the same driving task and the same traffic, an increasing environmental complexity yields higher ToC times in both conditions, with and without a secondary task. Our model of environmental complexity is a first step towards measuring the complexity of the real world, for a better prediction of ToC times in highly automated driving.

Today, ambitioned amateur athletes often do not have access to professional coaching but still invest great effort in becoming faster runners. Apart from a pure increase in the quantitative training load, a change of the running technique, e.g. transitioning from heel striking to fore- or midfoot running, can be highly effective and usually prevents knee-related injuries.

Today, ambitious amateur athletes often do not have access to professional coaching but still invest great effort in becoming faster runners. Apart from a pure increase in the quantitative training load, a change of the running technique, e.g. transitioning from heel striking to mid-/forefoot running, can be highly effective and usually prevents knee-related injuries.

Rock climbing involves complex movements and therefore requires guidance when acquiring a new technique. The classic approach is mimicking the movements of a more experienced climber. However, the trainee has to remember every nuance of the climb, since the sequence of movements cannot be performed in parallel to the experienced climber. As a solution to this problem, we present a video recording and replay system for climbing. The replay component allows for different in-situ video feedback methods. We investigated the video feedback component of the system by studying two example visualization techniques, i.e. a life-sized in-place projection and a real-time third-person view of the climber, augmented by a video showing a successful ascent. The latter is presented to the user on both Google Glass and a projected display. The results indicate that a life-sized projection was perceived as easiest to follow, while most of the climbers had problems with the context switches between the augmented video and the climbing wall. These findings can aid in the design of assistance systems that teach complex movements.

Wearable sports devices like GPS watches and heart rate monitors are ubiquitous in sports like running or road cycling and enable the users to receive real-time performance feedback. Although rock climbing is a trending sport, there are little to no consumer electronics available to support rock climbing training during exercise. In this paper, we investigated the acceptance and appropriateness of wearables in climbing on different body parts. Based on an online survey with 54 climbers, we designed a wearable device and conducted a perception study with 12 participants in a climbing gym. Using vibro-tactile, audible, and visual cues while climbing an easy route and a hard route, requiring high physical and cognitive load, we found that the most suited notification channel is sound, directly followed by vibro-tactile output. Light has been found to be inappropriate for the use in the sport of climbing.

Automakers are increasingly providing connectivity enhancements for vehicles to download navigational data, as well as to upload sensor information to the cloud. Generally, while more data may be better, for the driver on-the-go, information needs to be displayed in a manner that can be comprehended rather quickly. One of the major problems with visualizing route maps is that the amount of information visualized is always the same regardless of the fact that an individual may be more familiar with the region or whether an individual is driving at varying speeds. Research has shown that complex visualizations with visual clutter can cause cognitive overload that adversely affects the performance of a user. Additionally, the attention and interaction abilities of a driver are significantly compromised in a vehicular environment. We propose DriveSense, a context-sensitive visualization system that automatically varies the GPS updates and the corresponding visualization being displayed to the user based on the speed of the vehicle as well as the familiarity of the region that the user is driving in. Based on a user evaluation, we found that subjects preferred using the automatic visualizations of route maps generated by DriveSense than the visual representations shown by a standard GPS. We also computed visual clutter for our visualizations at varying speeds and found that the clutter was significantly less for the routes displayed by DriveSense for faster speeds as compared to slower speeds.