As automotive electronics continue to advance, cars are becoming more and more reliant on sensors to perform everyday driving operations. These sensors are omnipresent and help the car navigate, reduce accidents, and provide comfortable rides. However, they can also be used to learn about the drivers themselves. In this paper, we propose a method to predict, from sensor data collected at a single turn, the identity of a driver out of a given set of individuals. We cast the problem in terms of time series classification, where our dataset contains sensor readings at one turn, repeated several times by multiple drivers. We build a classifier to find unique patterns in each individual's driving style, which are visible in the data even on such a short road segment. To test our approach, we analyze a new dataset collected by AUDI AG and Audi Electronics Venture, where a fleet of test vehicles was equipped with automotive data loggers storing all sensor readings on real roads. We show that turns are particularly well-suited for detecting variations across drivers, especially when compared to straightaways. We then focus on the 12 most frequently made turns in the dataset, which include rural, urban, highway on-ramps, and more, obtaining accurate identification results and learning useful insights about driver behavior in a variety of settings.

This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach. Permanent repository link

This paper reports the experimental results of the acoustic rotation of spherical micro particles because of two orthogonal standing waves. When the standing waves are excited at equal frequency but with a phase shift between two external voltage signals there is an acoustic streaming around the particles. This streaming is due to a time averaging of the acoustic wave field and produces a nonzero viscous torque on the particles, driving them to rotate. The work investigates the micro-particle rotation due to the viscous torque and predict the particle's steady state rotational velocity. The previous theoretical discussions [Nyborg, J. Acoust. Soc. Am. 85, 329-339 (1958); Lee and Wang, J. Acoust. Soc. Am. 85, 1081-1088 (1989)] of the viscous torque on a non-rotating sphere are expanded to allow free rotations. The analytical calculations provide a deeper understanding of the viscous torque and explain the experimental observations of rotating particles. A macroscopic experimental device is designed to provide the necessary boundary conditions for the viscous torque to rotate spherical particles. The experiments not only show good agreement with the analysis, but also demonstrate that the viscous torque due to acoustic streaming may dominate for the case of near-spherical particle dynamics.

In recent years, cars have evolved from purely mechanical to veritable cyber-physical systems that generate large amounts of real-time data. This data is instrumental to the proper working of the vehicle itself, but makes them amenable to a multitude of other uses. For instance, GPS information has recently been used for a large number of mobility studies in the academic community [1]-[5], as well as to feed traffic apps such as Google Traffic TM and Waze TM. This use of vehicle data is already having a profound impact in science, industry, economy, and society at large. Now, imagine than instead of accessing one single source of vehicle-generated data (GPS), one can access the entire wealth of data exchanged on the Controller Area Network (CAN) bus in near real-time-amounting to over 4,000 signals sampled at high frequency, corresponding to a few Gigabytes of data per hour. What would be the implications, opportunities, and challenges sparked by this transition?

We present the first numerical simulation setup for the calculation of the acoustic viscous torque on arbitrarily shaped micro-particles inside general acoustic fields. Under typical experimental conditions, the particle deformation plays a minor role. Therefore, the particle is modeled as a rigid body which is free to perform any time-harmonic and time-averaged translation and rotation. Applying a perturbation approach, the viscoacoustic field around the particle is resolved to obtain the time-averaged driving forces for a subsequent acoustic streaming simulation. For some acoustic fields, the near-boundary streaming around the fluid-suspended particle induces surface forces on the nonrotating particle that integrate into a non-zero acoustic viscous torque. In the equilibrium state, this torque is compensated by an equal and opposite drag torque due to the particle rotation. The rotation-induced flow field is superimposed on the acoustic streaming field to obtain the total fluid motion around the rotating particle. In this work, we only consider cases within the Rayleigh limit even though the presented numerical model is not strictly limited to this regime. After a validation by analytical solutions, the numerical model is applied to challenging experimental cases. For an arbitrary particle density, we consider particle sizes that can be comparable to the viscous boundary layer thickness. This important regime has not been studied before because it lies beyond the validity limits of the available analytical solutions. The detailed numerical analysis in this work predicts nonintuitive phenomena, including an inversion of the rotation direction. Our numerical model opens the door to explore a wide range of experimentally relevant cases, including non-spherical particle rotation. As a step toward application fields such as micro-robotics, the rotation of a prolate ellipsoid is studied.