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Supplemental material, delaRosaOpenPracticesDisclosure for Two Ways to Facial Expression Recognition? Motor and Visual Information Have Different Effects on Facial Expression Recognition by Stephan de la Rosa, Laura Fademrecht, Heinrich H. Bülthoff, Martin A. Giese, and Cristóbal Curio in Psychological Science

The social context in which an action is embedded provides important information for the interpretation of an action. Is this social context integrated during the visual recognition of an action? We used a behavioural visual adaptation paradigm to address this question and measured participants' perceptual bias of a test action after they were adapted to one of two adaptors (adaptation after-effect). The action adaptation after-effect was measured for the same set of adaptors in two different social contexts. Our results indicate that the size of the adaptation effect varied with social context (social context modulation) although the physical appearance of the adaptors remained unchanged. Three additional experiments provided evidence that the observed social context modulation of the adaptation effect are owed to the adaptation of visual action recognition processes. We found that adaptation is critical for the social context modulation (experiment 2). Moreover, the effect is not mediated by emotional content of the action alone (experiment 3) and visual information about the action seems to be critical for the emergence of action adaptation effects (experiment 4). Taken together these results suggest that processes underlying visual action recognition are sensitive to the social context of an action. published

Recognizing social interactions, e.g. two people shaking hands, is important for obtaining information about other people and the surrounding social environment. Despite the visual complexity of social interactions, humans have often little difficulties to visually recognize social interactions. What is the visual representation of social interactions and the bodily visual cues that promote this remarkable human ability? Viewpoint dependent representations are considered to be at the heart of the visual recognition of many visual stimuli including objects (Bülthoff & Edelman, 1992), and biological motion patterns (Verfaillie, 1993). Here we addressed the question whether complex social actions acted out between pairs of people, e.g. hugging, are also represented in a similar manner. To this end, we created 3-D models from motion captured actions acted out by two people, e.g. hugging. These 3-D models allowed to present the same action from different viewpoints. Participants task was to discriminate a target action from distractor actions using a one-interval-forced-choice (1IFC) task. We measured participants' recognition performance in terms of reaction times (RT) and d-prime (d'). For each tested action we found one view that lead to superior recognition performance compared to other views. This finding demonstrates view-dependent effects of visual recognition, which are in line with the idea of a view dependent representations of social interactions. Subsequently, we examined the degree to which velocities of joints are able to predict the recognition performance of social interactions in order to determine candidate visual cues underlying the recognition of social interactions. We found that the velocities of the right arm, lower left leg, and both feet correlated with recognition performance.

Schon seit den 1980er Jahren ist das autonome Fahren ein entscheidendes For- schungsgebiet in der Informatik und Robotik. Dank jüngster Erkenntnisse, die et- wa durch die Bertha Benz Fahrt [ZBS+14] gewonnen wurden, rückt das Ziel des vollautonomen Fahrzeugs wieder in greifbare Nähe. Dennoch bleiben viele Fragen bisher unbeantwortet. Besonders nun, wo die Automobilindustrie in Richtung auto- nomer Fahrbetriebe für den Serieneinsatz drängt, bleibt weiterhin die Aufgabe der Fahrzeuglokalisierung mit serientauglichen Sensoren bisher unbeantwortet. Außer- dem müssen in diesem Fall Speicher- und Rechenressourcen auf einem Minimum gehalten werden. In dieser Arbeit wir das Simultaneous Localization and Mapping (SLAM) Problem für autonome Fahrszenarien auf einem Parkplatz näher analysiert. Dabei kommen vor allem Radio Detection and Ranging (RADAR) Sensoren zum Einsatz, die bisher zur Lokalisierung noch nicht im Detail analysiert wurden, obwohl sie zahlreich im Rahmen von Fahrerassistenzsystemen wie Abstandregeltempomaten zum Einsatz kommen. Da RADAR Sensoren ein hohes Grundrauschen haben, wurden diese bis- her nur spärlich bei den hochpräzisen Verfahren, wie sie für das autonome Fahren nötig sind, eingesetzt, weder in der Robotik noch in der Intelligent Transportation Systems Community. In dieser Arbeit wird gezeigt, dass der RADAR Sensor ein kosteneffizienter, robuster und präziser Sensor zur Fahrzeuglokalisierung sein kann, wenn seine physikalischen Eigenschaften korrekt modelliert sind. In diesem Zusammenhang wurde ein GraphSLAM basiertes Rahmenwerk entwickelt, das markante Punkte nur auf Basis von RADAR Daten extrahiert und so eine op- timierte Karte der Umgebung des RADAR Sensors generiert. Dieses wird weiterhin benutzt, um eine Crowd basierte Lokalisierung umzusetzen, welche nicht nur auf den RADAR Sensor limitiert ist. Indem weitere Sensoren, wie z.B. ein Laserscanner und eine Stereokamera, hinzugenommen wurden, konnte ein robustes Lokalisierungssys- tem entwickelt werden, welches das autonome Fahren auf dem Parkplatz ermöglicht und den hohen Genauigkeitsanforderungen gerecht wird. Es wird gezeigt, dass der RADAR Sensor eine entscheidende Rolle bei der durchgführten Sensorfusion spielt und somit die hohe Gesamtperformance erst möglich macht. 3 Die Ergebnisse dieser Arbeit werden auf einem Datensatz auf einem öffentlichen Parkplatz evaluiert, der über mehrere Monate hinweg verschiedenste Szenarien ab- deckt. Neben verschiedenen Wetterbedingungen und unterschiedlicher Parkplatzaus- lastung, wurden auch eine Reihe verschiedener Parkmanöver erfasst. Die Auswertung dieser Verfahren lässt die Aussage zu, dass der RADAR Sensor sich sowohl allein als auch in einem größeren Framework für das autonome Fahren eignet. Ever since the 1980s, researchers in computer science and robotics have been working on making autonomous cars. Due to recent breakthroughs in research and devel- opment, such as the Bertha Benz Project [ZBS+14], the goal of fully autonomous vehicles seems closer than ever before. Yet a lot of questions remain unanswered. Especially now that the automotive industry moves towards autonomous systems in series production vehicles, the task of precise localization has to be solved with automotive grade sensors and keep memory and processing consumption at a mini- mum. This thesis investigates the Simultaneous Localization and Mapping (SLAM) prob- lem for autonomous driving scenarios on a parking lot using low cost automotive sensors. The main focus is herby devoted to the RAdio Detection And Ranging (RADAR) sensor, which has not been widely analyzed in an autonomous driving scenario so far, even though they are abundant in the automotive industry for ap- plications such as Adaptive Cruise Control (ACC). Due to the high noise floor, the radar sensor has widely been disregarded in the Intelligent Transportation Systems and Robotics communities with regards to SLAM applications. However in this thesis, it is shown that the RADAR sensor proves to be an affordable, robust and precise sensor, when modeling its physical properties correctly. In this regard, a GraphSLAM based framework is introduced, which extracts features from the RADAR sensor and generates an optimized map of the surroundings using the RADAR sensor alone. This framework is used to enable crowd based localization, which is not limited to the RADAR sensor alone. By integrating an automotive Light Detection and Ranging (LiDAR) and stereo camera sensor, a robust and precise localization system can be built that that is suitable for autonomous driving even in complex parking lot scenarios. It it is thereby shown that the RADAR sensor is strongly contributing to obtaining good results in a sensor fusion setup. These results were obtained on an extensive dataset on a parking lot, which has been recorded over the course of several months. It contains different weather conditions, different configurations of parked cars and a multitude of different trajectories to validate the approaches described in this thesis and to come to the conclusion that the RADAR sensor is a reliable sensor in series autonomous driving systems, both in a multi sensor framework and as a single component for localization.