ZDIN-Zahlen im Detail

In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules for regulated locations can help various location-based applications in the context of Smart Cities, such as the accurate estimation of travel time and fuel consumption from a starting point to a destination. Traffic regulations as map features, however, are surprisingly still largely absent from maps, although they do affect traffic flow which, in turn, affects vehicle idling time at intersections, fuel consumption, CO2 emissions, and arrival time. In addition, mapping them using surveying equipment is costly and any update process has severe time constraints. This fact is precisely the motivation for this study. Therefore, its objective is to propose an automatic, fast, scalable, and inexpensive way to identify the type of intersection control (e.g., traffic lights, stop signs). A new method based on summarizing the collective behavior of vehicle crossing intersections is proposed. A modification of a well-known clustering algorithm is used to detect stopping and deceleration episodes. These episodes are then used to categorize vehicle crossing of intersections into four possible traffic categories (p1: free flow, p2: deceleration without stopping events, p3: only one stopping event, p4: more than one stopping event). The percentages of crossings of each class per intersection arm, together with other speed/stop/deceleration features, extracted from trajectories, are then used as features to classify the intersection arms according to their traffic control type (dynamic model). The classification results of the dynamic model are compared with those of the static model, where the classification features are extracted from OpenStreetMap. Finally, a hybrid model is also tested, where a combination of dynamic and static features is used, which outperforms the other two models. For each of the three models, two variants of the feature vector are tested: one where only features associated with a single intersection arm are used (one-arm model) and another where features also from neighboring intersection arms of the same intersection are used to classify an arm (all-arm model). The methodology was tested on three datasets and the results show that all-arm models perform better than single-arm models with an accuracy of 95% to 97%.

Voice commerce creates unprecedented opportuni­ties for consumers and vendor firms to interact, engage, and relate. With artificial intelligence–pow­ered voice assistants, consumers can make technol­ogy-mediated purchases without using their tactile senses, which represents a new space for commer­cial interactions. Drawing on the customer journey as an organizing framework, this article proposes a structured research agenda, in an attempt to shed light on the bright side effects of voice commerce while also acknowledging concerns for consumer protection and society in general. Voice assistants can enhance every stage of the purchase journey, yet their use might have negative consequences for customer relationships. In the prepurchase stage, voice assistants can act as information curators or manipulators. In the purchase stage, voice assis­tants can adopt roles as shopping concierges or impediments. In the postpurchase and usage stage, voice assistants can become trusted relationship partners or hostile intruders in consumers’ lives.

For the investigation of major traffic accidents, larger vessels are obliged to install a voyage data recorder (VDR). However, not every vessel is equipped with a VDR, and the readout is often a manual process that is costly. In addition, not only ship-related information can be relevant for reconstructing traffic accidents, but also information from other entities such as meteorological services or port operators. Moreover, another major challenge is that entities tend to trust only their records, and not those of others as these could be manipulated in favor of the particular recording entity (e.g., to disguise any damage caused). This paper presents an approach to documenting arbitrary data from different entities in a trustworthy, decentralized, and tamper-proof manner to support the conflict resolution process. For this purpose, all involved entities in a traffic situation can contribute to the documentation by persisting their available data. Since maritime stakeholders are equipped with various sensors, a diverse and meaningful data foundation can be aggregated. The data is then signed by a mutually agreed upon timestamping authority (TSA). In this way, everyone can cryptographically verify whether the data has been subsequently changed. This approach was successfully applied in practice by documenting a vessel’s mooring maneuver.

Participating in urban traffic is inherently risky for humans. Therefore, in psychology, behavioural studies have been using Virtual Reality (VR) to simulate and experiment with human behaviour. Safety critical interactions (e.g. conflict, collision or near collision) can be captured from the motion trajectories. However, the motion data in virtual settings is influenced by the modelling software used to create the virtual world, which might fail to capture one-to-one interactions accurately (such as interactions between pedestrians and cyclists in mixed traffic). Our system paper proposes a Pedestrianin-the-Loop (PIL) Mixed Reality (MR) framework, where mobile virtual cyclist avatars co-exist with humans in a real-world outdoor space. Such a setting can be used to study a pedestrian subject, both viewing and interacting with moving holograms of cyclists in real traffic. The novelty of our approach is modelling virtual avatars as cognitive agents. To achieve this, we integrate agent-based models so that the virtual avatar can sense the environment and interact with
the real user participating in the experiments. We demonstrate that this approach could contribute to effectively studying of pedestrian interactions. We also perform an evaluation to quantify the amount of trajectory error for our outdoor framework. For this, we compare the position data of a subject during an experiment to a proven benchmark for indoor motion capture. Additionally, an application of using the framework to demonstrate pedestrian dominance is presented.

Entwurf und Analyse eines Algorithmus zur Generierung von Collective Perception Nachrichten auf Basis der Trackinggenauigkeit von Objekten.