ComTec auf der Konferenz VTC 2015 - Fall

Der berühmte Hafen von Boston

In diesem Jahr fand die VTC - Fall vom 07. bis 09. September in Boston, US, statt. Neben interessanten Vorträgen bspw. aus den Bereichen '5G', 'Positioning and Localization' und 'Connected Vehicles' konnten wir drei Paper beitragen (s. Referenzen und Abstracts unten).

In diesem Jahr fand zudem erstmals der "Workshop on Mobile and Context Aware Services (MOCS)" statt. Der Workshop wurde parallel zur VTC 2015 - Fall in Boston durch geführt. Wir konnten uns über reges Interesse freuen. Einerseits hinsichtlich eingereichter Beiträge, andererseits hinsichtlich des Publikumsinteresses. Entsprechend sind viele gute Diskussion und Anregungen während und auch nach der Veranstaltung entstanden. Um dies weiterzuführen, wird der Workshop auch auf der nächsten VTC in Nanjing, China, stattfinden.

Neben der "Arbeit" hatten die Organisatoren der VTC auch für das "Vergnügen" gesorgt. Unter anderem wurden im Rahmen der Konferenz auch Hafenrundfahrten angeboten, auf denen Lobster (Hummer) serviert wurde, für den Boston bekannt ist. Das Tea-Party-Museum sowie eine Sightseeing-Tour haben uns weitere Einblicke in die, für amerikanische Verhältnisse, sehr alte Stadt gegeben.

Referenz: R. Kusber, A. Q. Memon, D. Kroll, and K. David, "Direction Detection of Users Independent of Smartphone Orientations", in IEEE Vehicular Technology Conf., Boston, USA, 2015.

Abstract: Smartphone sensors deliver useful information for applications such as indoor and outdoor navigation. An integral part of such applications is the detection of the orientation and movement direction of a smartphone user. Until now, movement direction detection using smartphones often relies on GPS, which is often not available indoors. Alternatively, other approaches use sensors such as accelerometer and compass instead. These approaches rely on carrying the smartphone in a predefined orientation, or knowing the orientation of the smartphone in relation to the orientation of the user. In his paper, we present an approach to detecting the orientation and movement direction of users carrying smartphones inside the trouser pocket. This approach first determines the orientation of the smartphone’s top using compass and orientation sensor. Second, the approach determines the orientation of the smartphone’s screen, and the user’s movement direction by observing compass and accelerometer during two steps the user takes. After these two steps, the approach is capable of continuously aligning smartphone orientation and user orientation. With our approach, the user is free to change direction, movement speed, or to stop moving at all. The smartphone can be placed in the trouser pocket arbitrarily. And the smartphone is free to wobble in the trouser pocket. How well our approach works, is investigated based on experimental measurements.

Referenz: A. Jahn, K. David, and S. Engel, "5G / LTE based protection of vulnerable road users: Detection of crossing a curb", in IEEE Vehicular Technology Conf., Boston, USA, 2015.

Abstract: Unfortunately, every year many vulnerable road users (VRUs), such as pedestrians or bicyclists are killed or seriously injured in traffic accidents. Several research groups are working on different solutions including radar or laser based approaches to reduce the number of traffic accidents VRUs are involved. The approach presented in this paper is based on wireless communications such as WLAN and 5G/LTE and a “context filter”. The “context filter” identifies vulnerable road users in potentially dangerous situations based on several contexts (VRU position, movement direction, accelerations). An identified dangerous situation is communicated between the VRU and the cars using wireless communication such as ad hoc or cellular systems. As one specifically interesting context for identifying dangerous situations this paper investigates on detecting pedestrians stepping onto the road. We examine smartphone sensor data and several reasoning classifiers to investigate whether “stepping onto the road” by detecting a pedestrian “crossing a curb” is possible. To the best of our knowledge detecting pedestrians crossing a curb by using smartphone sensors, has not been investigated so far.

Referenz: A. Jahn, S.L. Lau, K. David, and B. Sick, "A Toolchain for Context Recognition: Automating the Investigation of a Multitude of Parameter Sets", in 1st Int. Workshop on Mobile and Context Aware Services, Boston, USA, 7-9 Sept. 2015

Abstract: A person’s context data can be used for a multitude of applications, such as energy management or health care. Common context recognition approaches rely on several factors, such as the sensor set, features, or the context modeling algorithm. Discovering the recognition performances of different parameter setting combinations is a complex, time-consuming, and error-prone task. To support the context recognition research, we present the Context Recognition Assistance Tool (CRAT). The Context Recognition Assistance Tool assists by automatically conducting the evaluation for a multitude combination of parameter settings and clearly presents the findings. Using the CRAT, we investigate to what degree five parameters influence the recognition accuracy. To support the research in the field of context recognition, the CRAT is publicly available.