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09/09/2015

ComTec at the VTC 2015 conference - Fall

@Communication Technology
The famous harbor of Boston
The famous harbor of Boston

This year's VTC - Fall took place from September 7 to 9 in Boston, US. In addition to interesting presentations on topics such as '5G', 'Positioning and Localization' and 'Connected Vehicles', we were able to contribute three papers (see references and abstracts below).

This year also saw the first "Workshop on Mobile and Context Aware Services (MOCS)" take place. The workshop was held parallel to the VTC 2015 - Fall in Boston. We were pleased to see a great deal of interest. On the one hand in terms of the contributions submitted, and on the other in terms of the interest shown by the audience. Accordingly, many good discussions and suggestions were generated during and after the event. In order to continue this, the workshop will also be held at the next VTC in Nanjing, China.

In addition to the "work", the organizers of the VTC also took care of the "pleasure". Among other things, the conference also offered harbor cruises on which lobster, for which Boston is famous, was served. The Tea Party Museum and a sightseeing tour gave us further insights into the city, which is very old by American standards.

 

Reference: 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.

 

Reference: 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.

 

Reference: 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.